PTNet: An Improved Algorithm for Object Tracking

Chenglin Lu*, Yajuan Wei, Jin Li, Chuan Dai, Ying Liu, Zhijie Xu

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

Abstract

Pedestrian tracking, as a sub-task of object tracking, is widely applied in smart surveillance. However, the accuracy of object re-identification significantly decreases when objects encounter prolonged occlusion or undergo significant appearance changes. To address this issue, PTNet, a regression-based object tracking framework, is proposed in this paper. Firstly, a combination of DeepSORT and FastReid models is employed to resolve the problem of target re-identification after being occluded for a period of time. Secondly, a self-attention-based feature extraction model named BoTNet, incorporating the Global Multi-Head Self-Attention (MHSA), is integrated into the backbone network to capture global and long-distance features of targets in surveillance videos. Through ablation experiments, it was found that PTNet enhances accuracy by 4.8% compared to the benchmark. When compared to state-of-the-art object detection algorithms, PTNet demonstrates a significant improvement in performance.

Original languageEnglish
Title of host publicationICAC 2024 - 29th International Conference on Automation and Computing
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350360882
DOIs
Publication statusPublished - 2024
Externally publishedYes
Event29th International Conference on Automation and Computing, ICAC 2024 - Sunderland, United Kingdom
Duration: 28 Aug 202430 Aug 2024

Publication series

NameICAC 2024 - 29th International Conference on Automation and Computing

Conference

Conference29th International Conference on Automation and Computing, ICAC 2024
Country/TerritoryUnited Kingdom
CitySunderland
Period28/08/2430/08/24

Keywords

  • BoTNet
  • Deep Learning
  • mAP
  • Object tracking

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Lu, C., Wei, Y., Li, J., Dai, C., Liu, Y., & Xu, Z. (2024). PTNet: An Improved Algorithm for Object Tracking. In ICAC 2024 - 29th International Conference on Automation and Computing (ICAC 2024 - 29th International Conference on Automation and Computing). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICAC61394.2024.10718788