Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 30, Issue 12, Pages 3847-3852Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2019.2899588
Keywords
Feature extraction; Task analysis; Cameras; Noise measurement; Learning systems; Reinforcement learning; Feature aggregation; reinforcement learning (RL); sequential decision making; video-based person re-identification (re-id)
Categories
Funding
- National Key Research and Development Plan of China [2017YFB1300205]
- NSFC [61573222, 61801264]
- Major Research Program of Shandong Province [2018CXGC1503]
- Fundamental Research Funds of Shandong University [2016JC014]
- Basic Research Program of Shenzhen [JCYJ20170307153635551]
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Video-based person re-identification (re-id) matches two tracks of persons from different cameras. Features are extracted from the images of a sequence and then aggregated as a track feature. Compared to existing works that aggregate frame features by simply averaging them or using temporal models such as recurrent neural networks, we propose an intelligent feature aggregate method based on reinforcement learning. Specifically, we train an agent to determine which frames in the sequence should be abandoned in the aggregation, which can be treated as a decision making process. By this way, the proposed method avoids introducing noisy information of the sequence and retains these valuable frames when generating a track feature. On benchmark data sets, experimental results show that our method can boost the re-id accuracy obviously based on the state-of-the-art models.
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