TY - GEN
T1 - TaCoTrack
T2 - 35th Chinese Control and Decision Conference, CCDC 2023
AU - Wang, Zhixuan
AU - Wang, Bo
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The performance of visual object tracking generally depends on the extracted information of continuous frames. However, existing trackers cannot leverage temporal contexts to extract enough information from frames and does not adapt well to various challenges. In this paper, we present a neat and efficient framework, TaCoTrak, which completely exploits temporal contexts for object tracking. The temporal context are employed in two perspectives, the fusion of features and the refinement of search-response features. Specifically, for feature fusion, a dynamic self-adaptive convolution, which provides the capability of spatial feature representation, is designed to fuse the features extracted from multiple input frames with temporal information. For search-response feature refinement, we construct a temporal convolution with weights and bias change with each input. The extensive experiments fully demonstrate the effective and robust performance of TaCoTrak.
AB - The performance of visual object tracking generally depends on the extracted information of continuous frames. However, existing trackers cannot leverage temporal contexts to extract enough information from frames and does not adapt well to various challenges. In this paper, we present a neat and efficient framework, TaCoTrak, which completely exploits temporal contexts for object tracking. The temporal context are employed in two perspectives, the fusion of features and the refinement of search-response features. Specifically, for feature fusion, a dynamic self-adaptive convolution, which provides the capability of spatial feature representation, is designed to fuse the features extracted from multiple input frames with temporal information. For search-response feature refinement, we construct a temporal convolution with weights and bias change with each input. The extensive experiments fully demonstrate the effective and robust performance of TaCoTrak.
KW - Dynamic Convolution
KW - Object Tracking
KW - Temporal Context
UR - http://www.scopus.com/inward/record.url?scp=85181831263&partnerID=8YFLogxK
U2 - 10.1109/CCDC58219.2023.10327428
DO - 10.1109/CCDC58219.2023.10327428
M3 - Conference contribution
AN - SCOPUS:85181831263
T3 - Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023
SP - 4068
EP - 4073
BT - Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 20 May 2023 through 22 May 2023
ER -