Laser & Optoelectronics Progress, Volume. 57, Issue 18, 181501(2020)
Moving Object Tracking Algorithm Based on Depth Feature Adaptive Fusion
In this paper, we propose a moving target tracking algorithm based on the adaptive fusion of depth futures. This algorithm is aimed at solving the problems of poor anti-occlusion ability and robustness of traditional tracking algorithms in complex scenes. Considering the strong robustness of deep features and the advantages of high precision of shallow features, the deep sparse features are constructed using the sparse autoencoder to extract target features. Then, the depth features are adjusted according to the correlation information between adjacent frames as well as tracking confidence adaptive fusion with texture information to improve the tracker performance. To improve the robustness of the tracking algorithm while suppressing tracking drift when the confidence is lower than the set threshold, we introduce an improved speeded up robust features algorithm to locate the target. Experimental results show that the proposed algorithm has higher tracking accuracy, better robustness in occlusion scenes, and can effectively suppress tracking drift compared with the mainstream tracking algorithms.
Get Citation
Copy Citation Text
Rui Yang, Baohua Zhang, Yanyue Zhang, Xiaoqi Lü, Yu Gu, Yueming Wang, Xin Liu, Yan Ren, Jianjun Li. Moving Object Tracking Algorithm Based on Depth Feature Adaptive Fusion[J]. Laser & Optoelectronics Progress, 2020, 57(18): 181501
Category: Machine Vision
Received: Dec. 5, 2019
Accepted: Feb. 10, 2020
Published Online: Sep. 2, 2020
The Author Email: Zhang Baohua (zbh_wj2004@imust.cn)