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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.
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Xinchen Liu
Wu Liu
Huadóng Ma
Beijing University of Posts and Telecommunications
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Liu et al. (Fri,) studied this question.
www.synapsesocial.com/papers/69dd61cf0a7b4bc8c4101e32 — DOI: https://doi.org/10.1109/icme.2016.7553002