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Research on View-independent Object Classification and Indexing in Traffic Scene

Author: FengKe
Tutor: ChenLinQiang
School: Hangzhou University of Electronic Science and Technology
Course: Applied Computer Technology
Keywords: video analysis motion detect object tracking object classification perspective distortion object retrieval in video
CLC: TP391.41
Type: Master's thesis
Year: 2009
Downloads: 62
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Abstract


As the rapid development of the network, communications,microelectronic technology,a number of visual analysis system with specific function get the growing popularity of the pro-gaze for its intuitive, convenient and rich in content. Traffic monitoring is the most widely used area. However, the all-weather surveillance system captured a large number of video information, in which it is inefficient and mistakable to find the target with the use of pure-manual search methods. Therefore, it was hoped that the computer can analyze and understand video content, in order to achieve video analysis intelligent and practical system, video analysis techniques have emerged.Video analysis techniques deal primarily with video sequences which contain a variety of moving target, and detect, track, and classify and recognition goals, then understand and describe their behavior. Among them, target classification is an important aspect of the Video analysis with the content of classifying the object area based on motion analysis by their features of shape and motion, and is important to the development of high level video understanding techniques.Currently overseas research institutions and national universities have made some progress in the target classification technology, but there are still some constraints and lack of applications, in which the view-dependent issue of classification is the main factor affecting the stability of classification. The so-called view-dependent issue means 2D feature of object has some distortion because of projection, causing it is not correct for classification. The major work is described as follows:(1) Introduce basic theory of target classification and related research, including the expression of object features, common methods of object classification, moving target detection and tracking, and the use of scene knowledge.(2) The predict model based on kalman filter combine with maximum posterior probability for object matching is proposed to track moving objects with occlusions.(3) For the view-dependent problems encountered in target classification, Describes three representative kinds of 2D features rectification methods,and then promote 2D features recovery algorithm based on ground plane rectification. Based on the normalized characteristics, using Multi-Class Support Vector Machines (SVM) to achieve view-independent classification of moving object.(4) Research on moving object retrieval method based on video features,discuss the organization of feature data,the way of object retrieval and display form of retrival results.(5) By building the experiment platform based on Visual C++ and OpenCV graphics library. We verify the various algorithms with coding. Results shows the algorithm solve the view-dependent problems in object classification and achieve view-independent object classification.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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