期刊
NEURAL NETWORKS
卷 124, 期 -, 页码 223-232出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neunet.2020.01.012
关键词
Person re-identification; End-to-end; Feature pyramid optimization; Gradual Background Suppression
资金
- National Natural Science Foundation of China [61976166, 61772402, 61671339, U1605252, 61922066, 61876142]
- National Key Research and Development Program of China [2016QY01W0200, 2018AAA0103202]
- National High-Level Talents Special Support Program of China [CS31117200001]
- Fundamental Research Funds for the Central Universities, China [JB190120, JB190117]
- Young Talent fund of University Association for Science and Technology in Shaanxi, China [20180104]
- Tencent Open Fund, China
Compared with face recognition, the performance of person re-identification (re-ID) is still far from practical application. Among various interferences, there are two factors seriously limiting the performance improvement, i.e., the feature discriminability determined by external network effectiveness, and the image quality determined by internal background clutters. Target at the external network effectiveness problem, feature pyramids are effective to learn discriminative features because they can learn both detailed features from high-resolution shallow layers and semantical features from low-resolution deep layers, however, it can only achieve slight improvement on re-ID tasks because of the error back propagation problem. To handle the problem and utilize the effectiveness of feature pyramids, we propose a strategy called Feature Pyramid Optimization (FPO). Instead of concatenating features directly, the selected layers are optimized independently in a top-bottom order. Target at the internal background clutters problem, background suppression is generally considered for removing the environmental interference and improving the image quality. Several mask-based methods are used attempting to totally remove background clutters but achieve limited promotion because of the mask sharpening effect. We propose a novel strategy, i.e., Gradual Background Suppression (GBS) to reduce the background clutters and keep the smoothness of images simultaneously. Extensive experiments have been conducted and the results demonstrate the effectiveness of both FPO and GBS. (C) 2020 Elsevier Ltd. All rights reserved.
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