| 英文摘要 | Vehicle detection is a key technology in video-based traffic surveillance system. With the accelerating process of the urbanization and city scale expanding unceasingly, the recoverable amount of the automobile is also rising rapidly. Therefore, city traffic system becomes increasingly busy, resulting in traffic problems such as traffic safety, traffic congestion and environment pollution etc. In order to alleviate the traffic problems, in recent years, intelligent transportation systems (ITS) attract lots of attention throughout the world. Video-based traffic surveillance system, as an important part of ITS, plays an important role in alleviating traffic congestion, reducing the traffic accidents. However, the complex traffic environments have brought about huge challenges to vehicle detection, including vehicle occlusion and stationary vehicle under congestion scenarios, lighting variation under bad weathers and at different times of day. Aiming at solving the problems of existing traffic surveillance system and meeting the new demands for traffic surveillance, this thesis analyses the existing complex traffic system, studies vehicle detection technology from the point of view of object hierarchical structure and realizes the traffic surveillance system for the real traffic. Specifically, the main contributions of this thesis include the following aspects: 1) We analyze the salient parts of vehicle object and study the detection of these salient parts based on their distinctive features. In the urban traffic environment, vehicle detection technology often faces problems such as partial occlusion, intra-class variation, pose variation, background clutter and so on. It is not reasonable to detect the vehicle as a whole, while the part (local features) based vehicle detection method provides feasible ways to solve these problems. This thesis selects vehicle parts with small intra-class difference and large inter-class difference (i.e., significant parts) to represent the vehicle, including vehicle license plate and rear-lamps. After selecting the salient parts, we need to locate them through the discriminative appearance features. The vehicle license plate is detected based on its unique color and gradient features; as for vehicle rear-lamps, they are detected based on their unique color and regional characteristics. 2) Based on the probabilistic graphical model technology, we study the fusion of salient parts for vehicle detection. After detecting t... |
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