Feature generation based on relation learning and image partition for occluded person re-identification |
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| Affiliation: | 1. College of Mechanical Engineering, Anhui Polytechnic University, Wuhu 241000, China;2. State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China;1. School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia, 014010, China;2. School of Information Engineering, Mongolia Industrial University, Huhehaote, Inner Mongolia, 010051, China;3. Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, Baotou, Inner Mongolia, 014010, China;1. CBSR & NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing, China;2. University of Chinese Academy of Sciences, Beijing, China;3. Sichuan University, Chengdu, China;4. School of Engineering, Westlake University, Hangzhou, China;5. ZKTeco Co., Ltd, China |
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| Abstract: | In order to solve the challenging tasks of person re-identification(Re-ID) in occluded scenarios, we propose a novel approach which divides local units by forming high-level semantic information of pedestrians and generates features of occluded parts. The approach uses CNN and pose estimation to extract the feature map and key points, and a graph convolutional network to learn the relation of key points. Specifically, we design a Generating Local Part (GLP) module to divide the feature map into different units. Based on different occluded conditions, the partition mode of GLP has high flexibility and variability. The features of the non-occluded parts are clustered into an intermediate node, and then the spatially correlated features of the occluded parts are generated according to the de-clustering operation. We conduct experiments on both the occluded and the holistic datasets to demonstrate its effectiveness. |
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| Keywords: | Occluded person re-identification Graph convolutional network Partition |
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