TY - GEN
T1 - PTNet
T2 - 29th International Conference on Automation and Computing, ICAC 2024
AU - Lu, Chenglin
AU - Wei, Yajuan
AU - Li, Jin
AU - Dai, Chuan
AU - Liu, Ying
AU - Xu, Zhijie
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - BoTNet
KW - Deep Learning
KW - mAP
KW - Object tracking
UR - http://www.scopus.com/inward/record.url?scp=85208617484&partnerID=8YFLogxK
U2 - 10.1109/ICAC61394.2024.10718788
DO - 10.1109/ICAC61394.2024.10718788
M3 - Conference Proceeding
AN - SCOPUS:85208617484
T3 - ICAC 2024 - 29th International Conference on Automation and Computing
BT - ICAC 2024 - 29th International Conference on Automation and Computing
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 28 August 2024 through 30 August 2024
ER -