Large scale vehicle re identification in urban surveillance videos
Vehicle, as a significant object class in urban surveillance, attracts massive focuses in computer vision field, such as detection, tracking, and classification. Among them, vehicle re identification (Re Id) is an important yet frontier topic, which not only faces the challenges of enormous intra class and subtle inter class differences of vehicles in multicameras, but also suffers from the complicated environments in urban surveillance scenarios. Besides, the existing vehicle related datasets all neglect the requirements of vehicle Re Id: 1) massive vehicles captured in real world traffic environment; and 2) applicable recurrence rate to give cross camera vehicle search for vehicle Re Id. To facilitate vehicle Re Id research, we propose a large scale benchmark dataset for vehicle Re Id in the real world urban surveillance scenario, named "VeRi" It contains over 40,000 bounding boxes of 619 vehicles captured by 20 cameras in unconstrained traffic scene. Moreover, each vehicle is captured by 2~18 cameras in different viewpoints, illuminations, and resolutions to provide high recurrence rate for vehicle Re Id. Finally, we evaluate six competitive vehicle Re Id methods on VeRi and propose a baseline which combines the color, texture, and highlevel semantic information extracted by deep neural network.
2016 IEEE International Conference on Multimedia and Expo (ICME)