Attribute-guided attention and dependency learning for improving person re-identification based on data analysis technology

ABSTRACT

Person re-identification (Re-ID) can determine whether a pedestrian target can be matched across diverse regions or cameras, thereby alleviating the problem between massive surveillance data and inefficient manual retrieval. Inspired by attribute-person recognition (APR) network, this paper proposes an improved Re-ID method based on attribute learning, which uses an attribute-guided attention mechanism module and an attribute dependency learning module to learn fine-grained attribute features and rich dependencies among them. After that, a joint model with the integration of attribute recognition and person identity recognition is built for end-to-end training. Experimental results show that the proposed method can effectively improve Re-ID accuracy and achieve a competitive recognition performance.

KEYWORDS:

• Person re-identification
• attention mechanism
• attribute recognition
• intelligent surveillance
• big data analysis
• cloud computing

Acknowledgments

We thank Zheng et al. (2015), Ristani et al. (2016), and Lin et al. (2019) for providing person re-ID datasets and pedestrian attribute sets, and hyk1996 for providing the source code of attribute learning baseline model (https://github.com/hyk1996/Person-Attribute-Recognition-MarketDuke) on the GitHub website. They have significantly helped in preparing this paper.

Disclosure of potential conflicts of interest

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported in part by the National Natural Science Foundation of China (No. 61673395 and No. 61403415), the Zhengzhou Major Technological Innovation Project (No.188PCXZX773), the Shenzhen Science and Technology Plan (No.JCYJ20180307124010740, JSGG20200807171601010), the Graduate Education Reform Project of Shenzhen University (SZUGS2020JG11), and the Project of the Postgraduates Joint Training (LHPY-2020008, LHPY-2020007);the Zhengzhou Major Technological Innovation Project [188PCXZX773];the Shenzhen Science and Technology Plan [JCYJ20180307124010740, JSGG20200807171601010];the Graduate Education Reform Project of Shenzhen University [SZUGS2020JG11];the Project of the Postgraduates Joint Training [LHPY-2020008, LHPY-2020007];