# Group Members: Nuri Murat Arar Fatma Güney Aytuğ Murat Aydın M. Sami Arpa Erkam Akkurt. Asst. Prof. Dr. Pınar Duygulu Şahin

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1 Group Members: Nuri Murat Arar Fatma Güney Aytuğ Murat Aydın M. Sami Arpa Erkam Akkurt Supervisor: Jury Members: Asst. Prof. Dr. Selim Aksoy Prof. Dr. H. Altay Güvenir Asst. Prof. Dr. Pınar Duygulu Şahin Senior Project 2010, Computer Engineering, Bilkent University

2 Outline Introduction Stationary Camera Moving Camera Impacts of Our Engineering Solution Demo Questions and Answers

3 Introduction Introduction...

4 Overall Project Flow

5 Stationary Camera Video sequences coming from a stationary camera are used for target detection, classification and tracking. 1. Background Modeling 2. Target Detection 3. Feature Extraction 4. Target Classification 5. Tracking

6 Stationary Camera(cont.) 1. Background Modeling Default frame, used to determine whether an object is interesting Initially, built by first N frames Later, adaptively changes Adaptive Mixture of Gaussions Model

7 Stationary Camera(cont.) 2. Target Detection Finding differences between background model and each incoming frame Color based subtraction Incoming frame Foreground mask

8 Stationary Camera(cont.) 3. Feature Extraction Properties of detected targets in foreground mask Distance vectors calculated Feature Extraction Representation

9 Stationary Camera(cont.) 4. Target Classification Features of targets are compared with the reference list Absolute difference of vectors Best match is returned as label Best match is the target with the minimum difference a) b) c) Normalized Distance Vectors a) Car b) Person c) A Group of People

10 4. Target Classification (cont.) a) b) c) Normalized Distance Vectors a) Car b) Person c) A Group of People

11 Stationary Camera(cont.) 5. Tracking Matching the features of detected targets in consecutive frames Tracking Module in OpenCV Library

12 New Tools and Technologies Used MATLAB OpenCV Library Blob position correction Frames FG/BG Detection Module Blob Entering Detection Module Blob Tracking Module Trajectory PostProcessing Module Blobs (Id,Pos,Size) Trajectory Generation Module

13 Implementation Challenges Stationary Camera Video Compression effects Causing noises Illumination effects Changing colors because of different illumination Shadows of targets Enlarging foreground masks Camera Perspective problem Changing the size of targets along the road Occlusion of targets Overlapping foreground masks of different targets

14 Moving Camera Frame sequences captured from a moving camera are used for environment recognition. 1. Concurrent Visualization (Mosaics) AIM: Creating a panaromic image of environment 2. Target Recognition AIM: Target recognition on the created image

15 Moving Camera, Module Diagram

16 Concurrent Visualization (Mosaics) 1. Feature Detection 2. Feature Match 3. Transformation 4. Blend

17 1. Feature Detection Feature Detection

18 1. Feature Detection - David Lowe s SIFT (Scale-Invariant Feature Transform) algorithm Lowe, D. SIFT Keypoint Detector, University of British Columbia, - Average 2500 features are detected for a 640x480 image - Feature parameters double x; double y; double scl; double ori; struct feature* fwd_match; //x coord //y coord //scale of a Lowe-style feature //orientation of a Lowe-style feature //matching feature from forward image

19 2. Feature Match Feature Match

20 2. Feature Match - kd tree functions implemented by Beis, J. S. and Lowe, D. G J. S. and Lowe, D. G. Shape indexing using approximate nearest-neighbor search in high-dimensional spaces. In Conference on Computer Vision and Pattern Recognition (CVPR) - Average feature matches between two frames

21 3. Transformation Transformation

22 3. Transformation - Ransac transformation methods implemented by Rob Hess, Oregon State University Hess, R. Implementation of Ransac Transformation for Sift Features x 3 transformation matrix, used with cvwarpperspective function - Transformed image is returned

23 Concurrent Visualization

24 Target Recognition 1. Segmentation 2. Edge Detection 3. Line Fitting 4. Object Labeling

25 1. Segmentation - JSEG segmentation algorithm - Unsupervised Segmentation of Color- Texture Regions in Images 2. Edge Detection - Canny Edge Detection function is used

26 3. Line Fitting - Line fitting to find line segments. Source from Washington University, Computer Science, Computer Vision Course 4. Object Labeling -Sky detection: if region area / image area < 0.1 standard deviation < 22, rgb average > 150 if region area / image area > 0.1 standard deviation < 50, rgb average > 150 -Tree detection: independent color channel averages -Building detection: other segments

27

28

29

30 Demo

31 The End Thank you for listening

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