CDPM: convolutional deformable part models for semantically aligned person re-identification

Wang, K. and Ding, C. and Maybank, Stephen J. and Tao, D. (2019) CDPM: convolutional deformable part models for semantically aligned person re-identification. IEEE Transactions on Image Processing 29 , pp. 3416-3428. ISSN 1057-7149.

Abstract

Part-level representations are essential for robust person re-identification. However, common errors that arise during pedestrian detection frequently result in severe misalignment problems for body parts, which degrade the quality of part representations. Accordingly, to deal with this problem, we propose a novel method named Convolutional Deformable Part Models (CDPM). CDPM works by decoupling the complex part alignment procedure into two easier steps: first, a vertical alignment step detects each body part in the vertical direction, with the help of a multi-task learning model; second, a horizontal refinement step based on attention suppresses the background information around each detected body part. Since these two steps are performed orthogonally and sequentially, the difficulty of part alignment is significantly reduced. In the testing stage, CDPM is able to accurately align flexible body parts without any need for outside information. Extensive experimental results demonstrate the effectiveness of the proposed CDPM for part alignment. Most impressively, CDPM achieves state-of-the-art performance on three large-scale data sets: Market-1501, DukeMTMC-ReID, and CUHK03.

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