Pedestrian Tracking Algorithm Based on Convolutional Block Attention Module and Anchor-free Detection Network

ZHANG Hongying; HE Pengyi

doi:10.11999/JEIT210634

Volume 44 Issue 9

Sep. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(9): 3299-3307

ZHANG Hongying, HE Pengyi. Pedestrian Tracking Algorithm Based on Convolutional Block Attention Module and Anchor-free Detection Network[J]. Journal of Electronics & Information Technology, 2022, 44(9): 3299-3307. doi: 10.11999/JEIT210634

Citation:

ZHANG Hongying, HE Pengyi. Pedestrian Tracking Algorithm Based on Convolutional Block Attention Module and Anchor-free Detection Network[J]. Journal of Electronics & Information Technology, 2022, 44(9): 3299-3307. doi: 10.11999/JEIT210634

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Pedestrian Tracking Algorithm Based on Convolutional Block Attention Module and Anchor-free Detection Network

doi: 10.11999/JEIT210634 cstr: 32379.14.JEIT210634

ZHANG Hongying^,,
HE Pengyi

College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China

Funds: The National Key R&D Program of China(2018YFB1601200), Tianjin Graduate Scientific Research Innovation Project (2020YJSZXS14), The Special Plan for Sichuan Youth Scientific and Technological Innovation Research Team (2019JDTD0001)

Received Date: 2021-06-28
Rev Recd Date: 2021-09-14

Available Online: 2021-09-28

Publish Date: 2022-09-19

Abstract

Abstract

According to the target identity switch and tracking trajectory interruption, a multi-pedestrian tracking algorithm based on Convolutional Block Attention Module (CBAM) and anchor-free detection network is proposed. Firstly, attention mechanism is introduced to HrnetV2′s stem stage to extract more expressive features, thus strengthening the training of re-recognition branch. Secondly, in order to improve the operation speed of algorithm, detection task and recognition one share feature weights and are carried out simultaneously. Meanwhile, the convolutional channel’s number and parameter amount are reduced in the head network. Finally, the network is fully trained with proper parameters, and the algorithm is validated by multiple test sets. Experimental results show that compared with FairMOT, the accuracy of the proposed algorithm on 2DMOT15, MOT17 and MOT20 data sets is improved by 1.1%, 1.1%, 0.2% respectively, and the speed is improved by 0.82, 0.88 and 0.41 fps respectively. Compared with other mainstream algorithms, the proposed algorithm has the least number of target identity switching. The proposed algorithm improves effectively real-time performance of network model, which could be better applied to the scenes with severe occlusion.
- IDentity switch (IDs),
- High-resolution feature extraction network,
- Convolutional Block Attention Module (CBAM),
- Anchor-free detection netword,
- Head network,
- FairMOT

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References(17)

References

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