混合交通场景中的车辆检测和识别

介绍了一种城市交通流量视频检测系统及其车辆识别新算法。通过检测道路上的运动目标并利用目标的运动信息和外形特征进行车辆识别。考虑到目标的阴影会影响目标的形状特征 ,导致目标识别错误 ,提出了一种新的阴影检测算法———利用目标的灰度特征对运动目标进行阴影检测和分离。实验结果表明该系统在模拟城市混合交通环境下 ,能够克服目标阴影的影响 ,准确检测和识别车辆 ,同时能够满足实时性的要求

Vehicle Detection and Recognition in Multi-traffic Scenes

This paper presents a video-based urban traffic flow detection system and a novel vehicle recognition algorithm. Firstly, moving objects on the roadway are detected. Then, the vehicles are recognized based on their motion information and shape characteristics. Considering that objects shadows will impact the objects appearance and result in false recognition, a novel shadow detection approach is described: object shadows are detected and separated from the moving objects by using intensity characteristics. The experiment results show that the system can greatly reduce the shadows. impact,and accurately detect and recognize the vehicles on the urban multi-traffic road, while satisfiying the real-time requirement.