Person Reidentification Based on Adaptive Relation Attention Network in Intelligent Monitoring System for the IoB

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Abstract
The Internet of Behavior (IoB), which combines Internet of Things and artificial intelligence, plays an important role in building smart city. As a significant part of data collection and analysis, intelligent monitoring system needs robust algorithms for analyzing data to make corresponding feedback. The development of person reidentification (re-id) has benefited from deep learning methods, especially for the IoB application. However, most existing person re-id methods under intelligent monitoring cannot solve some problems in the real world, such as occlusion and background cluster. In this article, we propose an adaptive relation attention (ARA) network for person re-id based on the ARA mechanism, which can address the abovementioned challenges effectively. The ARA module consists of relation branch and adaption branch. Relation branch captures the global structural information by mining relation among feature nodes. Adaption branch generates dynamic weights for attention features that relation branch produced. Our constructed intelligent IoB system can acquire the behavior status of pedestrian at different times and places, providing corresponding feedback rapidly. Data transmission and storage in our system are built in a decentralized way, which is based on blockchain. Our person re-id method outperforms many state-of-the-art methods on the CUHK03, Market-1501, and DukeMTMC-reID, showing excellent robustness.
Funding Information
  • National Natural Science Foundation of China (61702226)
  • Natural Science Foundation of Jiangsu Province (BK20170200)
  • Innovative Research Foundation of Ship General Performance (14422102)
  • 111 Project, China (B12018)
  • Open Fund of Jiangsu Key Laboratory of Image and Video Understanding for Social Safety
  • Nanjing University of Science and Technology, Nanjing (J2021-7)

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