A part power set model for scale-free person retrieval

Recently, person re-identification (re-ID) has attracted increasing research attention, which has broad application prospects in video surveillance and beyond. To this end, most existing methods highly relied on well-aligned pedestrian images and hand-engineered part-based model on the coarsest feature map. In this paper, to lighten the restriction of such fixed and coarse input alignment, an end-to-end part power set model with multi-scale features is proposed, which captures the discriminative parts of pedestrians from global to local, and from coarse to fine, enabling part-based scale-free person re-ID. In particular, we first factorize the visual appearance by enumerating $k$-combinations for all $k$ of $n$ body parts to exploit rich global and partial information to learn discriminative feature maps. Then, a combination ranking module is introduced to guide the model training with all combinations of body parts, which alternates between ranking combinations and estimating an appearance model. To enable scale-free input, we further exploit the pyramid architecture of deep networks to construct multi-scale feature maps with a feasible amount of extra cost in term of memory and time. Extensive experiments on the mainstream evaluation datasets, including Market-1501, DukeMTMC-reID and CUHK03, validate that our method achieves the state-of-the-art performance.

Shen Yunhang, Ji Rongrong, Hong Xiaopeng, Zheng Feng, Guo Xiaowei, Wu Yongjian, Huang Feiyue

Publication type:
A4 Article in conference proceedings

Place of publication:
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, 10-16 August 2019 Macao, China

Applications of Supervised Learning Computer Vision, Categorization, computer vision, Deep Learning Computer Vision, detection, Indexing, Machine learning, Matching, Recognition, Retrieval, Semantic Interpretation Machine Learning Applications


Full citation:
Shen, Y., Ji, R., Hong, X., Zheng, F., Guo, X., Wu, Y., & Huang, F. (2019). A Part Power Set Model for Scale-Free Person Retrieval. Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence. Presented at the Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. https://doi.org/10.24963/ijcai.2019/471


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