TY - GEN
T1 - Multiple People Tracking Based on Improved SiameseFC Combined with Lightweight YOLO-V4
AU - Shen, Lu
AU - Chen, Zhiwen
AU - Zhang, Boliang
AU - Tang, Su Kit
AU - Mirri, Silvia
N1 - Publisher Copyright:
© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.
PY - 2024
Y1 - 2024
N2 - Multi-object tracking (MOT) is an active area of research in computer vision that is extensively applied in various domains, including but not limited to video surveillance, security, and intelligent transportation. There are two types of tracking algorithms: standard visual tracking techniques and deep learning tracking methods. Deep learning methods are becoming more common, but current tracking algorithms still need to overcome the challenge of false detection due to occlusion, similar backgrounds, and also the problem of slow speed. In response to the existing difficulties in multi-object tracking, this paper improves the fully convolutional Siamese (SiameseFC) network and integrates the Kalman filter to enhance the performance of the tracker. The lightweight network is used to improve the YOLO-V4 structure. The multi-people tracking network designed in this paper combines both networks, enabling objects to be detected and re-tracked after they reappear. By comparing with the performance of the network before improvement and other high-performance multi-object tracking algorithms, our proposed method can improve the processing speed of images while almost not losing too much precision, significantly reducing the model size.
AB - Multi-object tracking (MOT) is an active area of research in computer vision that is extensively applied in various domains, including but not limited to video surveillance, security, and intelligent transportation. There are two types of tracking algorithms: standard visual tracking techniques and deep learning tracking methods. Deep learning methods are becoming more common, but current tracking algorithms still need to overcome the challenge of false detection due to occlusion, similar backgrounds, and also the problem of slow speed. In response to the existing difficulties in multi-object tracking, this paper improves the fully convolutional Siamese (SiameseFC) network and integrates the Kalman filter to enhance the performance of the tracker. The lightweight network is used to improve the YOLO-V4 structure. The multi-people tracking network designed in this paper combines both networks, enabling objects to be detected and re-tracked after they reappear. By comparing with the performance of the network before improvement and other high-performance multi-object tracking algorithms, our proposed method can improve the processing speed of images while almost not losing too much precision, significantly reducing the model size.
KW - Multi-object tracking
KW - Object detection
KW - Object tracking
UR - http://www.scopus.com/inward/record.url?scp=85202347390&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-65123-6_21
DO - 10.1007/978-3-031-65123-6_21
M3 - Conference contribution
AN - SCOPUS:85202347390
SN - 9783031651229
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 291
EP - 305
BT - Quality, Reliability, Security and Robustness in Heterogeneous Systems - 19th EAI International Conference, QShine 2023, Proceedings
A2 - Leung, Victor C. M.
A2 - Li, Hezhang
A2 - Hu, Xiping
A2 - Ning, Zhaolong
PB - Springer Science and Business Media Deutschland GmbH
T2 - 19th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine 2023
Y2 - 8 October 2023 through 9 October 2023
ER -