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.

Authors:
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

Keywords:
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

Published:

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

DOI:
https://doi.org/10.24963/ijcai.2019/471

Read the publication here:
http://urn.fi/urn:nbn:fi-fe2020062645825