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Specific object re-identification across non-overlapping camera views in traffic accidents
Authors:Feigang Tan  Cong Zhai  Minglei Song  Rong Zhuang  Weiming Liu
Institution:1. ShenZhen Institute of Information Technology, School of Traffic and Environment, Shenzhen, Guangdong, People’s Republic of China;2. South China University of Technology, School of Civil Engineering and Transportation, Guangzhou 510641, People's Republic of China;3. China United Network Communications Corporation – Guangzhou branch, Guangzhou 510630, People's Republic of China
Abstract:ABSTRACT

To improve the robustness of object re-identification in complex outdoor environments for traffic safety systems, a novel object re-identification algorithm based on the Individual Similarity Difference Feature (ISDF) method is proposed. This method can provide reliable support for specific object tracking during traffic accidents in video surveillance networks. First, all the images in the gallery are divided into three parts according to a segmentation ratio, and six types of feature for each part are extracted. Second, prototypes for each feature of the three parts are constructed. Third, the image sequence of the same person is grouped, and then the ISDF is extracted from each image. Finally, we use the AdaBoost classifier to judge whether the two objects are matched and then output the final results. Extensive experiments are conducted on two public data sets (Eidgenössische Technische Hochschule Zürich and multi-camera object tracking). The performance of the object re-identification method is superior to the latest methods.
Keywords:Traffic safety systems  traffic accidents  across non-overlapping camera tracking  object re-identification  ISDF
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