结合注意力与特征融合的目标跟踪

doi:10.12052/gdutxb.190039

Abstract

Abstract: The full-convolutional Siamese network solves the problem of object tracking through similarity learning, and the algorithm has received more and more attention. In order to extract more discriminative object features and improve the accuracy and robustness of tracking, an object tracking model combining attention mechanism and feature fusion is proposed. Firstly, the first frame and the previous frame of the current frame are combined as target templates, and the features from multiple convolution layers of the target templates and the current frame are extracted by using the shared feature extraction network. Furthermore, for the multi-layer convolution features of the target templates, the channel attention mechanism is adopted to improve the discriminative power of the template features. Finally, the features of the target templates are cross-correlated with the features of the current frame to obtain response map, thereby obtaining the position and scale of the predicted object in the current frame. The final experimental results show that compared with several advanced tracking models, the proposed object tracking model achieves relatively competitive performance.

Key words: object tracking, siamese network, feature fusion, attention mechanism, discriminative feature

CLC Number:

TP931

Gao Jun-yan, Liu Wen-yin, Yang Zhen-guo. Object Tracking Combined with Attention and Feature Fusion[J].Journal of Guangdong University of Technology, 2019, 36(04): 18-23.

References

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