# Automatic parameter regulation for a tracking system with an auto-critical function

Save this PDF as:

Size: px
Start display at page:

Download "Automatic parameter regulation for a tracking system with an auto-critical function"

## Transcription

2 Input Fig. 2. K Control System y(t) optimized parameters no y(t) yes Regulation? Auto critical function Regulation Integration of the regulation module in a complex system. updates the parameters and reinitialises the modules. It is difficult to predict the performance gain of the autoregulation. Since the module can test only a discrete number of parameter settings, there is no guarantee that the global optimal parameter setting is found. For this reason, the goal of the regulation system is to find a parameter setting that increases system performance. Subsequent calls of the regulation module allow then to obtain a constantly increasing system performance. The modular architecture enables the use of different methods and apply the regulation to different system kinds. III. THE AUTO-CRITICAL FUNCTION The task of the auto-critical function is to provide a fast estimation of the current tracking performance. A performance evaluation function requires a reliable measure to estimate the current system performance. The used measure (described in Section III-B) is based on a probabilistic model of the scene which allows to estimate the likelihood of measurements. The probabilistic scene model is generated by a learning approach. Section III-C explains how the quality of a model can be measured. Section III-D discusses different clustering schemes. A. Learning a probabilistic model of a scene A model of a scene describes what usually happens in the scene. It describes a set of target positions and sizes, but also a set of paths of the targets within the scene. The model is computed from previously observed data. A valid model allows to describe everything that is going to be observed. For this reason we require that the training data is representative for what usually happens in the scene. The ideal model of a scene allows to decide in a probabilistic manner which measurements are typical and which measurements are unusual. With such a model we can compute the probability of single measurements and of temporal trajectories. Furthermore, we can detect outliers that occur due to measurement errors. The model represents the typical behaviour of the scene. Furthermore it enables the system to alert a user when unusual behavior takes place. This is a feature which is useful for the task of a video surveillance operator. In this section we describe the generation of a scene reference model which gives rise to a goodness measure that can compute the likelihood of measurements y(t i ) with respect to the scene reference model. We know that a single mode is insufficient to provide a valid scene description. We need a model with several modes that associate spatially close measurements and provide a locally valid model. The model is composed from data using a static camera. An important question is which training data should be used to create an initial model. The CAVIAR test case scenarios [4] contain 26 image sequences and hand labelled ground truth. We can use the ground truth to generate an initial model. If the initial model is not sufficient, the model can be refined by adding tracking observations where the measurements with low probability which are likely to contain errors are removed. For the computation of the scene reference model, we use the hand labelled data of the CAVIAR data set (42000 bounding boxes). We divide the model into a training and a test set of equal size. The observations consist of spatial measurements y spatial (t i ) = (µ x, µ y, σ 2 x, σ 2 y) (first and second moments of the target observation in frame I(t i )). We can extend these observations to spatio-temporal measurements y spatiotemp (t i ) = (µ x, µ y, σ 2 x, σ 2 y, µ x, µ y, σ 2 x, σ 2 y) by considering observations at subsequent time instances t i and t i 1. Such measurements have the advantage that we take into account the local motion direction and speed. A trajectory y(t) is a sequence of spatial or spatio-temporal measurements y(t i ). Single measurements are noted as vectors y(t i ) whereas trajectories y(t) are coded as vector lists. The following approach is valid for both types of observed trajectories y(t). To obtain a multi-modal model we have experimented with two types of clustering methods: k-means and k-means with pruning. K-means requires a fixed number of clusters that must be specified by the user a priori. K-means converges to a local minimum that depends on the initial clusters. These are determined randomly, which means that the algorithm produces different sub-optimal solutions in different runs. To overcome this problem, k-means is run several times with the same parameters. In section III-C we propose a measure to judge the quality of the clustering result. With this measure we select an optimal clustering solution as our scene reference model. The method k-means with pruning is a variation of the traditional k-means that produces stabler results due to subsequent fusion of close clusters. In this variation, k-means is called with a large number of clusters, k [500, 2000]. Clusters that are close within this solution are merged subsequently and clusters with few elements are considered as noise and removed. This method is less sensitive to outliers and has the characteristics of a hierarchical clustering scheme and at the same time can be computed quickly due to the initial fast k- means clustering. Figure 3 illustrates this algorithm. B. Evaluating the goodness of a trajectory A set of Gaussian clusters modeled by mean and covariance is an appropriate representation for statistical evaluation of measurements. The probability P ( y(t i ) C) can be computed according to equation 2.

3 developed accordingly. The probability of the sub-trajectories is defined as: merge clusters whose centers are closer than 1.0 delete clusters with < 4 elements Result 3 clusters and noise Fig. 3. K-means with pruning. After initial k-means clustering close clusters are merged and clusters with few elements are assigned to noise. The auto-regulation and auto-critical module need a measure to judge the goodness of a particular trajectory. A simple goodness score consists of the average probability of the most likely cluster for the single measurements. The goodness G(y(t)) of the trajectory y(t) = ( y(t n ),..., y(t 0 )) with length n + 1 is computed as follows: with G(y(t)) = 1 n + 1 n i=0 max k (P ( y(t i) C k )) (1) P ( y(t i ) C) = P ( y(t i ) µ; U) (2) 1 = T U 1 ( y(t (2π) dim/2 e( 0.5( y(ti) µ) i) µ)) U 1/2 with µ mean and U covariance of cluster C. Trajectories have variable length and may consist of several hundred measurements. The proposed goodness score is high for trajectories composed of likely measurements and small for trajectories that contain many unlikely measurements (errors). This measure allows to reliably classify good and bad trajectories independent of their particular length. On the other hand, the goodness score does not take into account the sequential structure of the measurements. The sequential structure is an important indicator for the detection of local measurement errors and errors due to badly adapted parameters. To study the potential of a goodness score that is sensitive to the sequential structure, we propose following measure (see equation 3). G seq(v) (y(t)) = 1 m m 1 i=0 log( P (y(s i ))) (3) which is the average log likelihood of the dominant term P (y(s)) of the probability of a sub-trajectory y(s) of length v. We use the log likelihood because P (y(s)) is typically very small. A trajectory y(t) = (y(s 0 ), y(s 1 ),..., y(s m 1 )) is composed of m sub-trajectories y(s i ) of length v. We develop the measure for v = 3, the measure for any other value v is P (y(s i )) = P ( y(t 2 ), y(t 1 ), y(t 0 )) = P (y(s i )) + r = P (C k2 y(t 2 ))P (C k1 y(t 1 ))P (C k0 y(t 0 )) P (C k2 C k1 C k0 ) + r (4) P (y(s i )) is composed of the probability of the most likely path through the modes of the model P (y(si )) plus a term r which contains the probability of all other path permutations. Naturally, the P (y(s i )) will be dominated by P (y(s i )), and r tends to be very small. This is the reason, why we use in the final goodness score only the dominant term P (y(s i )). P (C ki y(t i )) is computed using Bayes rule. The prior P (C k ) is set to the ratio C k /( u C u ). The normalisation factor P ( y(t i )) is constant. Since we are interested in the maximum likelihood, we compute: P (C ki y(t i )) = P ( y(t i) C ki )P (C ki ) P ( y(t i )) C ki P ( y(t i ) C ki ) u C u where C ki denotes the number of elements in C ki. P ( y(t i ) C ki ) is computed according to equation 2. The joint probability P (C k2 C k1 C k0 ) is developed according to P (C k2 C k1 C k0 ) = P (C k2 C k1 C k0 )P (C k1 C k0 )P (C k0 ) (6) We simplify this equation by assuming a Markov constraint of first order: P (C k2 C k1 C k0 ) = P (C k2 C k1 )P (C k1 C k0 )P (C k0 ) (7) To compute the conditional probabilities P (C i C j ), we need to construct a transfer matrix from the training set. This can be obtained by counting for each cluster C i the number of state changes and then normalise such that each line in the state matrix sums to 1. The probabilistically inspired sequential goodness score of equation 3 is computed using equations 4 to 7. C. Measuring the quality of the model K-means clustering is a popular tool for learning and model generation because the user needs to provide only the number of desired clusters [3], [7], [8]. K-means converges quickly to a (locally) optimal solution. K-means clustering starts from a number of randomly initialised cluster centers. Therefore, each run produces a different sub-optimal solution. In cases where the number of clusters is unknown, k-means can be run several times with varying number of clusters. A difficult problem is to rank the different k-means solutions and select the one that is the most appropriate for the task. This section provides a solution to this problem which is often neglected. For a particular model (clustering solution) we can compute the probability of a measurement belonging to the model. To ensure that the computed probability is meaningful, the (5)

4 model must be representative. A good model assigns a high probability to a typical trajectory and a low probability to an unusual trajectory. Based on these notions we define an evaluation criteria for measuring the quality of the model. We need to have a model that is neither too simple nor too complex. The complexity is related to the number of clusters [1]. A high number of clusters tends to over-fitting and a low number of clusters provides an imprecise description. Model quality evaluation requires a positive and negative example set. Typical target trajectories (positive examples) are provided within the training data. It is more difficult to create a negative example. A negative example trajectory is constructed as follows. First we measure the mean and variance of all training data. This represents the distribution of the data. We can now generate random measurements by drawing from this distribution with a random number generator. The result is a set of random measurements. From the training set, we generate a k-means clustering with a large number of clusters (K=100). For each random measurement we compute p( y(t i ) model 100 ). From the original random 5000 measurements we keep the 1200 measurements with the lowest probability. This gives the set of negative examples. Figure 4 shows an example of the positive and negative trajectory as well as the hand labelled ground truth and a multi-modal model obtained by k-means with pruning. For any positive and negative measurements we compute the probability P ( y(t i )). Classification of the measurements in positive and negative can be obtained by thresholding this value. For a threshold d the classification error can be computed according to equation 8. The optimal threshold, d, separates positive from negative measurements with a minimum classification error [1]. P d (error) = P (x R bad, C pos ) + P (x R good, C bad ) (8) = d 0 p(x C good )P (C good )dx + 1 d p(x C bad )P (C bad )dx with R bad = [0, d] and R good = [d, 1]. We search the optimal threshold d such that P d (error) is minimised. We operate on a histogram using logarithmic scale. This has the advantage that the distribution of lower values is sampled more densely. The optimal threshold d with minimum classification error can be estimated precisely with the method. This classification error P (error) is a measurement for the quality of the cluster model. Furthermore, less complex models should be preferred. For this reason we formulate the quality constraint of clustering solutions as follows: the best clustering has the lowest number of clusters and an error probability P (error) < q with q = 1%. The values of q are chosen depending on the task requirements. This measure is a fair evaluation criteria which enables to choose the best model among a set of k-means solutions. D. Clustering results We test two clustering methods: K-means and k-means with pruning. The positive trajectory is a person walking across the hall, the negative trajectory consists of 1200 measurements Subsequence of images Initial parameter setting Fig. 5. Scene reference model with metric Regulation process Parameter space exploration tool Optimized parameter setting A process for automatic parameter regulation. constructed as described above. The training set consists of hand labelled bounding boxes from 15 CAVIAR sequences (see Figure 4). Table I shows characteristics of the winning models with highest quality defined by minimum classification error and minimum number of clusters. The superiority of the k-means with pruning is demonstrated by the results. For the constraint P (error) < 1%, k-means with pruning requires only 20 or 19 clusters respectively whereas the classical k-means needs a model of clusters to obtain the same error rate. The best overall model is obtained for spatio-temporal measurements using k-means with pruning. IV. THE MODULE FOR AUTOMATIC PARAMETER REGULATION The task of the module for automatic regulation is to determine a parameter setting that improves the performance of the system. In the case of a detection and recognition system, this corresponds to increasing the number of true positives and reducing the number of false positives. For this task, the module requires an evaluation function of the current output, a strategy to choose a new parameter setting and a subsequence which can be replayed to optimize the performance. A. Integration When the parameter regulation module is switched on, the system tries to find a parameter setting that performs better than the current parameter setting on a subsequence that is provided by the tracking system. The system uses one of the goodness scores of section III-B. In the experiments we use a subsequence of 200 frames for auto-regulation. The tracker is run several times with changing parameter settings on this subsequence and the goodness score of the trajectory is measured for each parameter setting. The parameter setting that produces the highest goodness score is kept. Parameter settings are obtained from a parameter space exploration tool whose strategies are explained in the section IV-B and IV-C. The automatic regulation can only operate on sequences that produce a trajectory (something observable must happen in the scene). To allow a fair comparison, the regulation module must process the same subsequence several times. For this

5 Hand labelled bounding boxes (21000) Example of a typical trajectory Example of a unusual trajectory (random) Clustering result Fig. 4. Ground truth labelling for the entrance hall scenario, examples of typical and unusual trajectories and clustering result using k-means with pruning. Measurement type Clustering method # clusters optimal threshold d P (error) Spatial K-means K-means with pruning Spatio-temporal K-means K-means with pruning TABLE I BEST MODEL REPRESENTATIONS AND THEIR CHARACTERISTICS (FINAL NUMBER OF CLUSTERS, OPTIMAL THRESHOLD, AND CLASSIFICATION ERROR). reason the regulation process requires a significant amount of computing power. As a consequence, the regulation module should be run on a different host such that the regulation does not slow down the real time tracking. B. Parameter space exploration tool To solve the problem of the parameter space exploration we propose a parameter exploration tool that provides the next parameter setting to the regulation module. The dimensions of the parameter space as well as a reasonable range of the parameter values are given by the user. In our tracking example the parameter space is spanned by detection energy, density, sensitivity, split coefficient, α, and area threshold. In the experiments we tested two strategies for parameter setting selection. An enumerative method, that defines a small number of discrete values for each parameter. At each call the parameter space exploration tool provides the next parameter setting in the list. The disadvantage of this method is that only a small number of settings can be tested and the best setting may not be in the predefined list. The second strategy for parameter space exploration is based on a genetic algorithm. We found genetic algorithms perfectly adapted to our problem. It enables feedback from the performance of previous settings. We have a high dimensional feature space which makes hill climbing methods costly, whereas genetic algorithms explore the space without need of a high dimensional surface analysis. C. Genetic algorithm for parameter space exploration Among the different optimization schemes that exist we are looking for a particular method, that fulfills several constraints. We are not requiring to reach a global maximum of our function, but we would like to reach a good level of performance quickly. Furthermore we are not particularly interested in the shape of the surface in parameter space. We are only interested in obtaining a good payoff with a small number of tests. According to Goldberg [6], these are exactly the constraints of an application for which genetic algorithms are appropriate. Hill climbing methods are not feasible because the estimation of the gradient of a single point in a 6 dimensional space requires 2 6 tests. Testing several points would therefore require a higher number of tests than we would like. Genetic algorithms are inspired by the mechanics of natural selection. Genetic algorithms require an objective function to evaluate the performance of an individual and a coding of the input variables. Typically the coding is a binary string. In our example, each parameter is represented by 5 bit, which gives an input string of length 30. Genetic algorithms have three major operators: reproduction, crossover and mutation. Reproduction is a process in which individuals are copied according to their objective function values. Those individuals with high performance are copied more often than those with low performance. After reproduction, crossover is performed as follows. First, pairs of individuals are selected at random. Then, a position k within the string of length l is selected at random. Two new individuals are created by swapping all characters of position k + 1 to l. The mutation operator selects at random a position within the string and swaps its value. The power of genetic algorithms comes from the fact, that individuals with good performance are selected for reproduction and crossing of high performance individuals speculates on generating new ideas from high performance elements of past trials. For the initialisation of the genetic algorithm, the user needs to specify the boundaries of the input variable space, coding of the input variables, the size of the initial population and the probability of crossover and mutation. Goldberg [6] proposes to use a moderate population size, a high cross over

7 Auto-regulation method Recall Precision Total # true false targets positives positives Manual adaptation (benchmark) Spatio-temporal model, (genetic approach,g seq(10) ) Spatio temporal model, (genetic approach, simple score G) Spatio temporal model, (brute force, simple score G) Spatial model, (brute force, simple score G) No adaptation (low thresholds) No adaptation (high thresholds) TABLE II PRECISION AND RECALL OF THE DIFFERENT METHODS EVALUATED FOR 5 CAVIAR SEQUENCES (OVERLAP REQUIREMENT 50%). REFERENCES [1] C.M. Bishop. Neural Networks for Pattern Recognition. Oxford University Press, [2] A. Caporossi, D. Hall, P. Reignier, and J.L. Crowley. Robust visual tracking from dynamic control of processing. In International Workshop on Performance Evaluation of Tracking and Surveillance, pages 23 31, Prague, Czech Republic, May [3] G. Csurka, C. Bray, C. Dance, and L. Fan. Visual categorization with bags of keypoints. In European Conference on Computer Vision, Prague, Czech Republic, May [4] R.B. Fisher. The PETS04 surveillance ground-truth data sets. In International Workshop on Performance Evaluation of Tracking and Surveillance, Prague, Czech Republic, May [5] B. Géoris, F. Brémond, M. Thonnat, and B. Macq. Use of an evaluation and diagnosis method to improve tracking performances. In International Conference on Visualization, Imaging and Image Proceeding, September [6] D.E. Goldberg. Genetic Algorithms in Search and Optimization. Addison- Wesley, [7] T. Leung and J. Malik. Recognizing surfaces using three-dimensional textons. In International Conference on Computer Vision, Corfu, Greece, September [8] C. Schmid. Constructing models for content-based image retrieval. In IEEE Conference on Computer Vision and Pattern Recognition, volume 2, pages 39 45, Kauai, USA, December 2001.

### A Learning Based Method for Super-Resolution of Low Resolution Images

A Learning Based Method for Super-Resolution of Low Resolution Images Emre Ugur June 1, 2004 emre.ugur@ceng.metu.edu.tr Abstract The main objective of this project is the study of a learning based method

### The Scientific Data Mining Process

Chapter 4 The Scientific Data Mining Process When I use a word, Humpty Dumpty said, in rather a scornful tone, it means just what I choose it to mean neither more nor less. Lewis Carroll [87, p. 214] In

### PATTERN RECOGNITION AND MACHINE LEARNING CHAPTER 4: LINEAR MODELS FOR CLASSIFICATION

PATTERN RECOGNITION AND MACHINE LEARNING CHAPTER 4: LINEAR MODELS FOR CLASSIFICATION Introduction In the previous chapter, we explored a class of regression models having particularly simple analytical

### Predict Influencers in the Social Network

Predict Influencers in the Social Network Ruishan Liu, Yang Zhao and Liuyu Zhou Email: rliu2, yzhao2, lyzhou@stanford.edu Department of Electrical Engineering, Stanford University Abstract Given two persons

### A New Robust Algorithm for Video Text Extraction

A New Robust Algorithm for Video Text Extraction Pattern Recognition, vol. 36, no. 6, June 2003 Edward K. Wong and Minya Chen School of Electrical Engineering and Computer Science Kyungpook National Univ.

### Journal of Industrial Engineering Research. Adaptive sequence of Key Pose Detection for Human Action Recognition

IWNEST PUBLISHER Journal of Industrial Engineering Research (ISSN: 2077-4559) Journal home page: http://www.iwnest.com/aace/ Adaptive sequence of Key Pose Detection for Human Action Recognition 1 T. Sindhu

### Statistical Machine Learning

Statistical Machine Learning UoC Stats 37700, Winter quarter Lecture 4: classical linear and quadratic discriminants. 1 / 25 Linear separation For two classes in R d : simple idea: separate the classes

### Tracking in flussi video 3D. Ing. Samuele Salti

Seminari XXIII ciclo Tracking in flussi video 3D Ing. Tutors: Prof. Tullio Salmon Cinotti Prof. Luigi Di Stefano The Tracking problem Detection Object model, Track initiation, Track termination, Tracking

### Tracking Groups of Pedestrians in Video Sequences

Tracking Groups of Pedestrians in Video Sequences Jorge S. Marques Pedro M. Jorge Arnaldo J. Abrantes J. M. Lemos IST / ISR ISEL / IST ISEL INESC-ID / IST Lisbon, Portugal Lisbon, Portugal Lisbon, Portugal

### VEHICLE LOCALISATION AND CLASSIFICATION IN URBAN CCTV STREAMS

VEHICLE LOCALISATION AND CLASSIFICATION IN URBAN CCTV STREAMS Norbert Buch 1, Mark Cracknell 2, James Orwell 1 and Sergio A. Velastin 1 1. Kingston University, Penrhyn Road, Kingston upon Thames, KT1 2EE,

### Outlier Ensembles. Charu C. Aggarwal IBM T J Watson Research Center Yorktown, NY 10598. Keynote, Outlier Detection and Description Workshop, 2013

Charu C. Aggarwal IBM T J Watson Research Center Yorktown, NY 10598 Outlier Ensembles Keynote, Outlier Detection and Description Workshop, 2013 Based on the ACM SIGKDD Explorations Position Paper: Outlier

### CS231M Project Report - Automated Real-Time Face Tracking and Blending

CS231M Project Report - Automated Real-Time Face Tracking and Blending Steven Lee, slee2010@stanford.edu June 6, 2015 1 Introduction Summary statement: The goal of this project is to create an Android

### Example: Credit card default, we may be more interested in predicting the probabilty of a default than classifying individuals as default or not.

Statistical Learning: Chapter 4 Classification 4.1 Introduction Supervised learning with a categorical (Qualitative) response Notation: - Feature vector X, - qualitative response Y, taking values in C

### Lecture 20: Clustering

Lecture 20: Clustering Wrap-up of neural nets (from last lecture Introduction to unsupervised learning K-means clustering COMP-424, Lecture 20 - April 3, 2013 1 Unsupervised learning In supervised learning,

### Environmental Remote Sensing GEOG 2021

Environmental Remote Sensing GEOG 2021 Lecture 4 Image classification 2 Purpose categorising data data abstraction / simplification data interpretation mapping for land cover mapping use land cover class

### DATA MINING CLUSTER ANALYSIS: BASIC CONCEPTS

DATA MINING CLUSTER ANALYSIS: BASIC CONCEPTS 1 AND ALGORITHMS Chiara Renso KDD-LAB ISTI- CNR, Pisa, Italy WHAT IS CLUSTER ANALYSIS? Finding groups of objects such that the objects in a group will be similar

### Categorical Data Visualization and Clustering Using Subjective Factors

Categorical Data Visualization and Clustering Using Subjective Factors Chia-Hui Chang and Zhi-Kai Ding Department of Computer Science and Information Engineering, National Central University, Chung-Li,

### Mining the Software Change Repository of a Legacy Telephony System

Mining the Software Change Repository of a Legacy Telephony System Jelber Sayyad Shirabad, Timothy C. Lethbridge, Stan Matwin School of Information Technology and Engineering University of Ottawa, Ottawa,

### Classification Problems

Classification Read Chapter 4 in the text by Bishop, except omit Sections 4.1.6, 4.1.7, 4.2.4, 4.3.3, 4.3.5, 4.3.6, 4.4, and 4.5. Also, review sections 1.5.1, 1.5.2, 1.5.3, and 1.5.4. Classification Problems

### Christfried Webers. Canberra February June 2015

c Statistical Group and College of Engineering and Computer Science Canberra February June (Many figures from C. M. Bishop, "Pattern Recognition and ") 1of 829 c Part VIII Linear Classification 2 Logistic

### Machine Learning and Pattern Recognition Logistic Regression

Machine Learning and Pattern Recognition Logistic Regression Course Lecturer:Amos J Storkey Institute for Adaptive and Neural Computation School of Informatics University of Edinburgh Crichton Street,

### CCNY. BME I5100: Biomedical Signal Processing. Linear Discrimination. Lucas C. Parra Biomedical Engineering Department City College of New York

BME I5100: Biomedical Signal Processing Linear Discrimination Lucas C. Parra Biomedical Engineering Department CCNY 1 Schedule Week 1: Introduction Linear, stationary, normal - the stuff biology is not

### CS 2750 Machine Learning. Lecture 1. Machine Learning. http://www.cs.pitt.edu/~milos/courses/cs2750/ CS 2750 Machine Learning.

Lecture Machine Learning Milos Hauskrecht milos@cs.pitt.edu 539 Sennott Square, x5 http://www.cs.pitt.edu/~milos/courses/cs75/ Administration Instructor: Milos Hauskrecht milos@cs.pitt.edu 539 Sennott

### Tracking performance evaluation on PETS 2015 Challenge datasets

Tracking performance evaluation on PETS 2015 Challenge datasets Tahir Nawaz, Jonathan Boyle, Longzhen Li and James Ferryman Computational Vision Group, School of Systems Engineering University of Reading,

### A Study on SURF Algorithm and Real-Time Tracking Objects Using Optical Flow

, pp.233-237 http://dx.doi.org/10.14257/astl.2014.51.53 A Study on SURF Algorithm and Real-Time Tracking Objects Using Optical Flow Giwoo Kim 1, Hye-Youn Lim 1 and Dae-Seong Kang 1, 1 Department of electronices

### Vision based Vehicle Tracking using a high angle camera

Vision based Vehicle Tracking using a high angle camera Raúl Ignacio Ramos García Dule Shu gramos@clemson.edu dshu@clemson.edu Abstract A vehicle tracking and grouping algorithm is presented in this work

### Data Mining - Evaluation of Classifiers

Data Mining - Evaluation of Classifiers Lecturer: JERZY STEFANOWSKI Institute of Computing Sciences Poznan University of Technology Poznan, Poland Lecture 4 SE Master Course 2008/2009 revised for 2010

### Social Media Mining. Data Mining Essentials

Introduction Data production rate has been increased dramatically (Big Data) and we are able store much more data than before E.g., purchase data, social media data, mobile phone data Businesses and customers

### Azure Machine Learning, SQL Data Mining and R

Azure Machine Learning, SQL Data Mining and R Day-by-day Agenda Prerequisites No formal prerequisites. Basic knowledge of SQL Server Data Tools, Excel and any analytical experience helps. Best of all:

### 6.2.8 Neural networks for data mining

6.2.8 Neural networks for data mining Walter Kosters 1 In many application areas neural networks are known to be valuable tools. This also holds for data mining. In this chapter we discuss the use of neural

### Classifying Manipulation Primitives from Visual Data

Classifying Manipulation Primitives from Visual Data Sandy Huang and Dylan Hadfield-Menell Abstract One approach to learning from demonstrations in robotics is to make use of a classifier to predict if

### A MACHINE LEARNING APPROACH TO FILTER UNWANTED MESSAGES FROM ONLINE SOCIAL NETWORKS

A MACHINE LEARNING APPROACH TO FILTER UNWANTED MESSAGES FROM ONLINE SOCIAL NETWORKS Charanma.P 1, P. Ganesh Kumar 2, 1 PG Scholar, 2 Assistant Professor,Department of Information Technology, Anna University

### Real-Time Tracking of Pedestrians and Vehicles

Real-Time Tracking of Pedestrians and Vehicles N.T. Siebel and S.J. Maybank. Computational Vision Group Department of Computer Science The University of Reading Reading RG6 6AY, England Abstract We present

### Linear Classification. Volker Tresp Summer 2015

Linear Classification Volker Tresp Summer 2015 1 Classification Classification is the central task of pattern recognition Sensors supply information about an object: to which class do the object belong

### Clustering. Adrian Groza. Department of Computer Science Technical University of Cluj-Napoca

Clustering Adrian Groza Department of Computer Science Technical University of Cluj-Napoca Outline 1 Cluster Analysis What is Datamining? Cluster Analysis 2 K-means 3 Hierarchical Clustering What is Datamining?

### Tracking Algorithms. Lecture17: Stochastic Tracking. Joint Probability and Graphical Model. Probabilistic Tracking

Tracking Algorithms (2015S) Lecture17: Stochastic Tracking Bohyung Han CSE, POSTECH bhhan@postech.ac.kr Deterministic methods Given input video and current state, tracking result is always same. Local

### Guido Sciavicco. 11 Novembre 2015

classical and new techniques Università degli Studi di Ferrara 11 Novembre 2015 in collaboration with dr. Enrico Marzano, CIO Gap srl Active Contact System Project 1/27 Contents What is? Embedded Wrapper

### Component Ordering in Independent Component Analysis Based on Data Power

Component Ordering in Independent Component Analysis Based on Data Power Anne Hendrikse Raymond Veldhuis University of Twente University of Twente Fac. EEMCS, Signals and Systems Group Fac. EEMCS, Signals

### Machine Learning for Naive Bayesian Spam Filter Tokenization

Machine Learning for Naive Bayesian Spam Filter Tokenization Michael Bevilacqua-Linn December 20, 2003 Abstract Background Traditional client level spam filters rely on rule based heuristics. While these

### IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 20, NO. 7, JULY 2009 1181

IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 20, NO. 7, JULY 2009 1181 The Global Kernel k-means Algorithm for Clustering in Feature Space Grigorios F. Tzortzis and Aristidis C. Likas, Senior Member, IEEE

### Algorithm (DCABES 2009)

People Tracking via a Modified CAMSHIFT Algorithm (DCABES 2009) Fahad Fazal Elahi Guraya, Pierre-Yves Bayle and Faouzi Alaya Cheikh Department of Computer Science and Media Technology, Gjovik University

### Practical Data Science with Azure Machine Learning, SQL Data Mining, and R

Practical Data Science with Azure Machine Learning, SQL Data Mining, and R Overview This 4-day class is the first of the two data science courses taught by Rafal Lukawiecki. Some of the topics will be

### Face Recognition using SIFT Features

Face Recognition using SIFT Features Mohamed Aly CNS186 Term Project Winter 2006 Abstract Face recognition has many important practical applications, like surveillance and access control.

### Statistical Machine Learning from Data

Samy Bengio Statistical Machine Learning from Data 1 Statistical Machine Learning from Data Gaussian Mixture Models Samy Bengio IDIAP Research Institute, Martigny, Switzerland, and Ecole Polytechnique

### MACHINE LEARNING IN HIGH ENERGY PHYSICS

MACHINE LEARNING IN HIGH ENERGY PHYSICS LECTURE #1 Alex Rogozhnikov, 2015 INTRO NOTES 4 days two lectures, two practice seminars every day this is introductory track to machine learning kaggle competition!

### Data a systematic approach

Pattern Discovery on Australian Medical Claims Data a systematic approach Ah Chung Tsoi Senior Member, IEEE, Shu Zhang, Markus Hagenbuchner Member, IEEE Abstract The national health insurance system in

### Flat Clustering K-Means Algorithm

Flat Clustering K-Means Algorithm 1. Purpose. Clustering algorithms group a set of documents into subsets or clusters. The cluster algorithms goal is to create clusters that are coherent internally, but

### Introduction to Artificial Neural Networks. Introduction to Artificial Neural Networks

Introduction to Artificial Neural Networks v.3 August Michel Verleysen Introduction - Introduction to Artificial Neural Networks p Why ANNs? p Biological inspiration p Some examples of problems p Historical

### Cheng Soon Ong & Christfried Webers. Canberra February June 2016

c Cheng Soon Ong & Christfried Webers Research Group and College of Engineering and Computer Science Canberra February June (Many figures from C. M. Bishop, "Pattern Recognition and ") 1of 31 c Part I

### Class #6: Non-linear classification. ML4Bio 2012 February 17 th, 2012 Quaid Morris

Class #6: Non-linear classification ML4Bio 2012 February 17 th, 2012 Quaid Morris 1 Module #: Title of Module 2 Review Overview Linear separability Non-linear classification Linear Support Vector Machines

### Assessment. Presenter: Yupu Zhang, Guoliang Jin, Tuo Wang Computer Vision 2008 Fall

Automatic Photo Quality Assessment Presenter: Yupu Zhang, Guoliang Jin, Tuo Wang Computer Vision 2008 Fall Estimating i the photorealism of images: Distinguishing i i paintings from photographs h Florin

### Logistic Regression. Jia Li. Department of Statistics The Pennsylvania State University. Logistic Regression

Logistic Regression Department of Statistics The Pennsylvania State University Email: jiali@stat.psu.edu Logistic Regression Preserve linear classification boundaries. By the Bayes rule: Ĝ(x) = arg max

### Search Taxonomy. Web Search. Search Engine Optimization. Information Retrieval

Information Retrieval INFO 4300 / CS 4300! Retrieval models Older models» Boolean retrieval» Vector Space model Probabilistic Models» BM25» Language models Web search» Learning to Rank Search Taxonomy!

### Distance based clustering

// Distance based clustering Chapter ² ² Clustering Clustering is the art of finding groups in data (Kaufman and Rousseeuw, 99). What is a cluster? Group of objects separated from other clusters Means

### Hybrid Evolution of Heterogeneous Neural Networks

Hybrid Evolution of Heterogeneous Neural Networks 01001110 01100101 01110101 01110010 01101111 01101110 01101111 01110110 01100001 00100000 01110011 01101011 01110101 01110000 01101001 01101110 01100001

### 1 Maximum likelihood estimation

COS 424: Interacting with Data Lecturer: David Blei Lecture #4 Scribes: Wei Ho, Michael Ye February 14, 2008 1 Maximum likelihood estimation 1.1 MLE of a Bernoulli random variable (coin flips) Given N

### Data Mining for Model Creation. Presentation by Paul Below, EDS 2500 NE Plunkett Lane Poulsbo, WA USA 98370 paul.below@eds.

Sept 03-23-05 22 2005 Data Mining for Model Creation Presentation by Paul Below, EDS 2500 NE Plunkett Lane Poulsbo, WA USA 98370 paul.below@eds.com page 1 Agenda Data Mining and Estimating Model Creation

### STA 4273H: Statistical Machine Learning

STA 4273H: Statistical Machine Learning Russ Salakhutdinov Department of Statistics! rsalakhu@utstat.toronto.edu! http://www.cs.toronto.edu/~rsalakhu/ Lecture 6 Three Approaches to Classification Construct

### D-optimal plans in observational studies

D-optimal plans in observational studies Constanze Pumplün Stefan Rüping Katharina Morik Claus Weihs October 11, 2005 Abstract This paper investigates the use of Design of Experiments in observational

### Machine Learning Logistic Regression

Machine Learning Logistic Regression Jeff Howbert Introduction to Machine Learning Winter 2012 1 Logistic regression Name is somewhat misleading. Really a technique for classification, not regression.

### Ensemble Methods. Knowledge Discovery and Data Mining 2 (VU) (707.004) Roman Kern. KTI, TU Graz 2015-03-05

Ensemble Methods Knowledge Discovery and Data Mining 2 (VU) (707004) Roman Kern KTI, TU Graz 2015-03-05 Roman Kern (KTI, TU Graz) Ensemble Methods 2015-03-05 1 / 38 Outline 1 Introduction 2 Classification

### Evaluation of local spatio-temporal features for action recognition

Evaluation of local spatio-temporal features for action recognition Heng WANG 1,3, Muhammad Muneeb ULLAH 2, Alexander KLÄSER 1, Ivan LAPTEV 2, Cordelia SCHMID 1 1 LEAR, INRIA, LJK Grenoble, France 2 VISTA,

### An Introduction to Machine Learning

An Introduction to Machine Learning L5: Novelty Detection and Regression Alexander J. Smola Statistical Machine Learning Program Canberra, ACT 0200 Australia Alex.Smola@nicta.com.au Tata Institute, Pune,

### Chapter 6. The stacking ensemble approach

82 This chapter proposes the stacking ensemble approach for combining different data mining classifiers to get better performance. Other combination techniques like voting, bagging etc are also described

### Real-time Visual Tracker by Stream Processing

Real-time Visual Tracker by Stream Processing Simultaneous and Fast 3D Tracking of Multiple Faces in Video Sequences by Using a Particle Filter Oscar Mateo Lozano & Kuzahiro Otsuka presented by Piotr Rudol

### Robotics 2 Clustering & EM. Giorgio Grisetti, Cyrill Stachniss, Kai Arras, Maren Bennewitz, Wolfram Burgard

Robotics 2 Clustering & EM Giorgio Grisetti, Cyrill Stachniss, Kai Arras, Maren Bennewitz, Wolfram Burgard 1 Clustering (1) Common technique for statistical data analysis to detect structure (machine learning,

### Java Modules for Time Series Analysis

Java Modules for Time Series Analysis Agenda Clustering Non-normal distributions Multifactor modeling Implied ratings Time series prediction 1. Clustering + Cluster 1 Synthetic Clustering + Time series

### EFFICIENT DATA PRE-PROCESSING FOR DATA MINING

EFFICIENT DATA PRE-PROCESSING FOR DATA MINING USING NEURAL NETWORKS JothiKumar.R 1, Sivabalan.R.V 2 1 Research scholar, Noorul Islam University, Nagercoil, India Assistant Professor, Adhiparasakthi College

### DIGITAL video is an integral part of many newly emerging

782 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 14, NO. 6, JUNE 2004 Video Object Segmentation Using Bayes-Based Temporal Tracking and Trajectory-Based Region Merging Vasileios

### CLASSIFICATION AND CLUSTERING. Anveshi Charuvaka

CLASSIFICATION AND CLUSTERING Anveshi Charuvaka Learning from Data Classification Regression Clustering Anomaly Detection Contrast Set Mining Classification: Definition Given a collection of records (training

### Neural Networks. CAP5610 Machine Learning Instructor: Guo-Jun Qi

Neural Networks CAP5610 Machine Learning Instructor: Guo-Jun Qi Recap: linear classifier Logistic regression Maximizing the posterior distribution of class Y conditional on the input vector X Support vector

### Image Compression through DCT and Huffman Coding Technique

International Journal of Current Engineering and Technology E-ISSN 2277 4106, P-ISSN 2347 5161 2015 INPRESSCO, All Rights Reserved Available at http://inpressco.com/category/ijcet Research Article Rahul

### Modelling, Extraction and Description of Intrinsic Cues of High Resolution Satellite Images: Independent Component Analysis based approaches

Modelling, Extraction and Description of Intrinsic Cues of High Resolution Satellite Images: Independent Component Analysis based approaches PhD Thesis by Payam Birjandi Director: Prof. Mihai Datcu Problematic

### Probabilistic Latent Semantic Analysis (plsa)

Probabilistic Latent Semantic Analysis (plsa) SS 2008 Bayesian Networks Multimedia Computing, Universität Augsburg Rainer.Lienhart@informatik.uni-augsburg.de www.multimedia-computing.{de,org} References

### Data quality in Accounting Information Systems

Data quality in Accounting Information Systems Comparing Several Data Mining Techniques Erjon Zoto Department of Statistics and Applied Informatics Faculty of Economy, University of Tirana Tirana, Albania

### Predicting the Stock Market with News Articles

Predicting the Stock Market with News Articles Kari Lee and Ryan Timmons CS224N Final Project Introduction Stock market prediction is an area of extreme importance to an entire industry. Stock price is

### Evolutionary Detection of Rules for Text Categorization. Application to Spam Filtering

Advances in Intelligent Systems and Technologies Proceedings ECIT2004 - Third European Conference on Intelligent Systems and Technologies Iasi, Romania, July 21-23, 2004 Evolutionary Detection of Rules

### INTRODUCTION TO NEURAL NETWORKS

INTRODUCTION TO NEURAL NETWORKS Pictures are taken from http://www.cs.cmu.edu/~tom/mlbook-chapter-slides.html http://research.microsoft.com/~cmbishop/prml/index.htm By Nobel Khandaker Neural Networks An

### Face Recognition using Principle Component Analysis

Face Recognition using Principle Component Analysis Kyungnam Kim Department of Computer Science University of Maryland, College Park MD 20742, USA Summary This is the summary of the basic idea about PCA

### Advanced Ensemble Strategies for Polynomial Models

Advanced Ensemble Strategies for Polynomial Models Pavel Kordík 1, Jan Černý 2 1 Dept. of Computer Science, Faculty of Information Technology, Czech Technical University in Prague, 2 Dept. of Computer

### The Impact of Big Data on Classic Machine Learning Algorithms. Thomas Jensen, Senior Business Analyst @ Expedia

The Impact of Big Data on Classic Machine Learning Algorithms Thomas Jensen, Senior Business Analyst @ Expedia Who am I? Senior Business Analyst @ Expedia Working within the competitive intelligence unit

### COMP3420: Advanced Databases and Data Mining. Classification and prediction: Introduction and Decision Tree Induction

COMP3420: Advanced Databases and Data Mining Classification and prediction: Introduction and Decision Tree Induction Lecture outline Classification versus prediction Classification A two step process Supervised

### Face Recognition in Low-resolution Images by Using Local Zernike Moments

Proceedings of the International Conference on Machine Vision and Machine Learning Prague, Czech Republic, August14-15, 014 Paper No. 15 Face Recognition in Low-resolution Images by Using Local Zernie

### W6.B.1. FAQs CS535 BIG DATA W6.B.3. 4. If the distance of the point is additionally less than the tight distance T 2, remove it from the original set

http://wwwcscolostateedu/~cs535 W6B W6B2 CS535 BIG DAA FAQs Please prepare for the last minute rush Store your output files safely Partial score will be given for the output from less than 50GB input Computer

### Search and Information Retrieval

Search and Information Retrieval Search on the Web 1 is a daily activity for many people throughout the world Search and communication are most popular uses of the computer Applications involving search

### KEITH LEHNERT AND ERIC FRIEDRICH

MACHINE LEARNING CLASSIFICATION OF MALICIOUS NETWORK TRAFFIC KEITH LEHNERT AND ERIC FRIEDRICH 1. Introduction 1.1. Intrusion Detection Systems. In our society, information systems are everywhere. They

### Why the Normal Distribution?

Why the Normal Distribution? Raul Rojas Freie Universität Berlin Februar 2010 Abstract This short note explains in simple terms why the normal distribution is so ubiquitous in pattern recognition applications.

### Bayesian Image Super-Resolution

Bayesian Image Super-Resolution Michael E. Tipping and Christopher M. Bishop Microsoft Research, Cambridge, U.K..................................................................... Published as: Bayesian

### Linear Threshold Units

Linear Threshold Units w x hx (... w n x n w We assume that each feature x j and each weight w j is a real number (we will relax this later) We will study three different algorithms for learning linear

### Lecture 3: Linear methods for classification

Lecture 3: Linear methods for classification Rafael A. Irizarry and Hector Corrada Bravo February, 2010 Today we describe four specific algorithms useful for classification problems: linear regression,

### Mapping an Application to a Control Architecture: Specification of the Problem

Mapping an Application to a Control Architecture: Specification of the Problem Mieczyslaw M. Kokar 1, Kevin M. Passino 2, Kenneth Baclawski 1, and Jeffrey E. Smith 3 1 Northeastern University, Boston,

### Recognizing Cats and Dogs with Shape and Appearance based Models. Group Member: Chu Wang, Landu Jiang

Recognizing Cats and Dogs with Shape and Appearance based Models Group Member: Chu Wang, Landu Jiang Abstract Recognizing cats and dogs from images is a challenging competition raised by Kaggle platform

### Message-passing sequential detection of multiple change points in networks

Message-passing sequential detection of multiple change points in networks Long Nguyen, Arash Amini Ram Rajagopal University of Michigan Stanford University ISIT, Boston, July 2012 Nguyen/Amini/Rajagopal

### Object tracking & Motion detection in video sequences

Introduction Object tracking & Motion detection in video sequences Recomended link: http://cmp.felk.cvut.cz/~hlavac/teachpresen/17compvision3d/41imagemotion.pdf 1 2 DYNAMIC SCENE ANALYSIS The input to

### Colour Image Segmentation Technique for Screen Printing

60 R.U. Hewage and D.U.J. Sonnadara Department of Physics, University of Colombo, Sri Lanka ABSTRACT Screen-printing is an industry with a large number of applications ranging from printing mobile phone

### Overview. Evaluation Connectionist and Statistical Language Processing. Test and Validation Set. Training and Test Set

Overview Evaluation Connectionist and Statistical Language Processing Frank Keller keller@coli.uni-sb.de Computerlinguistik Universität des Saarlandes training set, validation set, test set holdout, stratification

### Information Retrieval and Web Search Engines

Information Retrieval and Web Search Engines Lecture 7: Document Clustering December 10 th, 2013 Wolf-Tilo Balke and Kinda El Maarry Institut für Informationssysteme Technische Universität Braunschweig

### Towards better accuracy for Spam predictions

Towards better accuracy for Spam predictions Chengyan Zhao Department of Computer Science University of Toronto Toronto, Ontario, Canada M5S 2E4 czhao@cs.toronto.edu Abstract Spam identification is crucial