TY - GEN
T1 - Dynamic Small Target Detection and Tracking Based on Hierarchical Network and Adaptive Input Image Stream
AU - Chi, Yucan
AU - Bai, Chengchao
AU - Guo, Jifeng
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022
Y1 - 2022
N2 - Small target detection and dynamic target continuous tracking are difficulties in the field of target detection and tracking respectively. The experimental results show that the smaller the target size is, the lower the detection accuracy is, the more dynamic the target is, and the lower the tracking accuracy is. This paper presents a high dynamic small target detection and tracking system which is composed of a yolov4 network, KCF tracker, and Kalman filter prediction algorithm. In this system, the detection network is divided into global and local levels, in which the global network is used for initial detection and re-detection after tracking failure, while the local detection network and tracker are used for real-time tracking of small targets, a more robust system is constructed. Compared with the original target detection network, this method weakens the influence of external factors such as the change of target shape and the fast-moving speed on the detection accuracy. The recognition rate of the small target is more than 96%, and the detection accuracy achieves 78%.
AB - Small target detection and dynamic target continuous tracking are difficulties in the field of target detection and tracking respectively. The experimental results show that the smaller the target size is, the lower the detection accuracy is, the more dynamic the target is, and the lower the tracking accuracy is. This paper presents a high dynamic small target detection and tracking system which is composed of a yolov4 network, KCF tracker, and Kalman filter prediction algorithm. In this system, the detection network is divided into global and local levels, in which the global network is used for initial detection and re-detection after tracking failure, while the local detection network and tracker are used for real-time tracking of small targets, a more robust system is constructed. Compared with the original target detection network, this method weakens the influence of external factors such as the change of target shape and the fast-moving speed on the detection accuracy. The recognition rate of the small target is more than 96%, and the detection accuracy achieves 78%.
KW - Adaptive input mechanism
KW - Dynamic tracking
KW - Small target detection
UR - https://www.scopus.com/pages/publications/85130874898
U2 - 10.1007/978-981-16-9492-9_324
DO - 10.1007/978-981-16-9492-9_324
M3 - 会议稿件
AN - SCOPUS:85130874898
SN - 9789811694912
T3 - Lecture Notes in Electrical Engineering
SP - 3297
EP - 3307
BT - Proceedings of 2021 International Conference on Autonomous Unmanned Systems, ICAUS 2021
A2 - Wu, Meiping
A2 - Niu, Yifeng
A2 - Gu, Mancang
A2 - Cheng, Jin
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Autonomous Unmanned Systems, ICAUS 2021
Y2 - 24 September 2021 through 26 September 2021
ER -