Vehicle Detection Using Dynamic Bayesian Networks

Transcription

1 Vehicle Detection Using Dynamic Bayesian Networks Sourabh Dekate BhushanRade Dipak Mahajan Sangita Chaudhari A B S T R A C T We present an automatic vehicle detection system for aerial or ground surveillance. The purpose is to design a pixel-wise classification method for vehicle detection. Here we perform pixel-wise classification; still relations among neighboring pixels in a region are preserved in the feature extraction process. This paper encloses the description and flaws in the existing methods and technologies applied for vehicle detection, basic problem definition, approach and analysis of the new system which will be employed, design and technologies which will be utilized. For better results we will first eliminate the possibility of environment noise as much as possible. This can be achieved by removing the background using color histogram. Carefully studying the color histogram will give the details of those colors which are occurring frequently in the video, these color are considered background and thus removed. Then features are extracted like corner or edge and color transformations etc. To perform pixel-wise classification Dynamic Bayesian Networks will be used and further post processing done using morphological operations. Index Terms:Vehicle detection; Dynamic Bayesian Network; Pixel-wise classification; Feature Extraction; Histogram; I. INTRODUCTION Object detection is the process of finding instances of real-world objects such as faces, bicycles, and buildings in images or videos. Here in our case, the object is a vehicle. Object detection algorithms typically use extracted features and learning algorithms to recognize instances of an object category. There are various applications for an efficient vehicle detection system. These applications must use automated system as manually identifying the vehicle is a very tedious and time consuming. Few of the applications are, traffic monitoring, security, drive through vehicle detection (Fig. 1), vehicle separation, military. The challenges of vehicle detection in include camera motions such as panning, tilting, and rotation. So here we try to compose a new way to detect vehicles which will negate these issues , IJAFRSE and VIVRUTI 2015 All Rights Reserved

2 II. EXISTING SYSTEMS Fig 1.Vehicle detection In the field of technical obstacle detected by vision system, two approaches existed: the first approach is unicameral approach that uses a single camera that consists of an image interpretation with former knowledge of information about these obstacles. This information can be texture information color. The second one is the stereo or multi-camera approach which is based on the variation map after matching primitives between different views of the sensor. Vehicle detection algorithms have two basic step; Hypothesis Generation (HG) and Hypothesis Verification (HV) [1]. In the hypothesis Generation step, the algorithm hypothesizes the locations of vehicles in an image. In the Hypothesis Verification (HV) step, the algorithm verifies the presence of vehicle in an image. The methods in the HG step can be categorized into tree methods; Knowledge-based methods which use symmetry of object, color, corners and edges; Stereo-vision-based methods which use two cameras; Motion-based Methods which track the motion of pixels between the consecutive frames. The methods in the HV step are Template-based methods and Appearance methods. Template-based methods use predefined patterns of the vehicle class. Appearancebased methods include pattern classification system between vehicle and non-vehicle. There are a many works tackling realtime on-road vehicle detection problem. All the papers used monocular cameras and have realtime constraints. Betke et al. [2] have used horizontal and vertical edges (Knowledge-based methods) in HG step. The selected regions at HG step are matched with predefined template in HV step. Sun et al. [3]have used horizontal and vertical edges in HG step. However, they use Haar Wavelet Transform and SVMs (Appearancebased methods) in HV step. Wedel et al. [4] detected long-distance stationary obstacles including vehicles. They used an efficient optical flow algorithm in HG step. They used Sum of squared differences (SSD) with a threshold value to verify their hypothesis. Hinz and Baumgartner utilized a hierarchical model that describes different levels of details of vehicle features. Extraction is based on a hierarchical model that describes the prominent vehicle features on different levels in detail. Besides these object properties, the model comprises contextual knowledge, i.e., relations between the vehicle and objects[5]. Lin et al. proposed a method by subtracting background colors of each frame and then refined vehicle candidate regions by enforcing size constraints of vehicles. However, they assumed too many parameters. Such prior assumptions of parameters will cause miss detection as it is not possible to store every possible size, shape etc. of any vehicle. For this method to be used, we require a large amount of sample in training data which is not realistic[6] , IJAFRSE and VIVRUTI 2015 All Rights Reserved

3 Choi and Yang proposed a vehicle detection algorithm using the symmetric property of car shapes. But it is prone to miss detection as not always the vehicles will have a symmetric shape. The shape may lose its symmetry with the change in angle of camera. Thus they used shape descriptors, but this makes the system inflexible as not each and every vehicle will have similar shape as of that in database. The algorithm also used color segmentation, but it may cause problems when the vehicle is multi-colored. Then again, the color segmentation algorithm is quiet complex which will take time to execute[7]. III. PROPOSED SYSTEM To design a new vehicle detection framework that preserves the advantages of the existing works and avoids their drawbacks. The proposed system is shown in fig. 2. The framework should be divided into the training phase and the detection phase. In the training phase, extracting multiple features including local edge and corner features, as well as vehicle colors to train a dynamic Bayesian network (DBN). In the detection phase, first performing background color removal. Afterward, the same feature extraction procedure should be performed as in the training phase. The extracted features will serve as the evidence to infer the unknown state of the trained DBN, which will indicate whether a pixel belongs to a vehicle or not. The distinguishing feature of the proposed framework would be that the detection task is based on pixel wise classification. However, the features will be extracted in a neighbourhood region of each pixel. Therefore, the extracted features will comprise not only pixel-level information but also relationship among neighbouring pixels in a region (generating Images). Such design will be more effective and efficient than region-based or multi scale sliding window detection methods. These phases are explained in detail below. A. Background Color Removal Initially the video will be given as input and the frames will be generated dynamically. We then construct the color histogram of each frame and remove the colors that appear most frequently in the scene. These removed pixels do not need to be considered in subsequent detection processes. Performing background color removal cannot only reduce false alarms but also speed up the detection process. B. Feature Extraction In this phase, the local features are to be extracted for the use of DBN. These features include the edges and colors. Features can be analyzed using edges and corners. Methods such as Canny edge detectors [8] can come in handy or even adaptive threshold can be used. C. Dynamic Bayesian Network When the features are extracted, these can be used as the training data for the bayesian network. As we will use bayesian network, the amount of training data required will be less. In the training stage, we obtain the conditional probability tables of the DBN model via expectation-maximization algorithm by providing the ground-truth labeling of each pixel and its corresponding observed features from several training videos [9]. In the detection phase, the Bayesian rule is used to obtain the probability that a pixel belongs to a vehicle , IJAFRSE and VIVRUTI 2015 All Rights Reserved

4 IV. RESULTS AND DISCUSSIONS Fig 2. Block diagram of proposed system We have managed to come half way to the target as initial phases are completed. Initially with the help of MATLAB, we have extracted the individual frames (fig. 3). Now all the processing will be done on these frames. The first phase is removal of background. The removal of background is done with the help of creation of parent histogram. This histogram is created using all the histograms of the individual frames. Why is this histogram necessary? Because it gives us the idea which colors are repeating in the entire video. The colors which are constant throughout the video will be more in number. Thus the histogram will show which pixels belong to background (fig. 4). This histogram makes it easy to remove the frequently occurring colors. The threshold to remove the colors should be such that most of the background must be removed leaving the less occurring vehicle pixels intact. Fig 3. Original frame , IJAFRSE and VIVRUTI 2015 All Rights Reserved

5 Fig 4. Parent histogram As it can be seen in figure 5, most of the background is removed. The next part is defining the edges so that the features can be extracted for further processing. Here, we use canny edge detector as it is one of the better edge detecting methods. Fig 5. Background Removed Fig 6. Defining edges , IJAFRSE and VIVRUTI 2015 All Rights Reserved

6 Now as the edges are prominently displayed as shown in figure 6. It is ready for applying dynamic bayesian networks which will classify the pixels if they belong to the vehicle or not. But DBN cannot be directly applied to the edges. The data needed is in terms of co-ordinates of the edge pixel, so that it can be seen which edges move and which do not. V. CONCLUSION In this paper, we are not performing region-based classification, which would highly depend on computational intensive color segmentation algorithms such as mean shift. Instead, we have proposed a pixel-wise classification method for the vehicle detection using DBNs. In spite of performing pixel-wise classification, relations among neighbouring pixels in a region are preserved in the feature extraction process. Therefore, the extracted features comprise not only pixel-level information but also region-level information. The number of frames required to train the DBN is very small. Overall, the entire framework does not require a large amount of training samples. It is clear that the systems existing for the detection of vehicles are not perfect, there is space for improvements. Some of the major drawbacks would be that the current systems do not work with multiple camera angle or prior movements of the camera. This system would make those improvements and remove the drawbacks of the existing ones. VI. FUTURE WORK The GUI will be designed on MATLAB itself which could be done easily. The important parts of the coding are be the feature extraction (local and color transformation), classification using DBN and post processing. This will be implemented in MATLAB. Performing vehicle tracking on the detected vehicles can further stabilize the detection results. Automatic vehicle detection and tracking could serve as the foundation for event analysis in intelligent aerial surveillance systems. VII. REFERENCE [1] R. Miller, On-road vehicle detection: A review, IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 28, no. 5, pp , [2] M. Betke, E. Haritaoglu, and L. Davis, Real-time multiple vehicledetection and tracking from a moving vehicle, Machine Vision and Applications, vol. 12, no. 2, pp , [3] G. B. Sun, R. Miller and D. DiMeo, A real-time precrash vehicledetection system, In Proceedings of IEEE Workshop onapplications of Computer Vision, pp , [4] Wedel, U. Franke, J. Klappstein, T. Brox, and D. Cremers, Realtimedepth estimation and obstacle detection from monocular video, Pattern Recognition, vol. 4174, pp , [5] R. Lin, X. Cao, Y. Xu, C. Wu, and H. Qiao, Airborne moving vehicle detection for urban traffic surveillance, In Proceedings of 11th International IEEE Conference on Intelligent Transportation Systems, pp , [6] S. Hinz and A. Baumgartner, Vehicle detection in aerial images using generic features, grouping, and context, Pattern Recognition vol. 2191, pp , [7] J. Y. Choi and Y. K. Yang, Vehicle detection from aerial images using local shape information, Advances in Image and Video Technology, pp , [8] J. F. Canny, A computational approach to edge detection, IEEE Transactions on Pattern Analysis and Machine Intelligence,, no. 6, pp ,1986. [9] L. Junning, Dynamic Bayesian Networks, PhD Thesis, University of British Columbia, Vancouver, , IJAFRSE and VIVRUTI 2015 All Rights Reserved