Lightweight correlation filter based object tracking network

Xingyu Mu; Siwei Wang; Hui Chen; Xinjing Zhang; Xin Xu; Yunfeng Li

doi:10.1117/12.3021275

19 February 2024 Lightweight correlation filter based object tracking network

Xingyu Mu, Siwei Wang, Hui Chen, Xinjing Zhang, Xin Xu, Yunfeng Li

Proceedings Volume 13063, Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023); 130631Z (2024) https://doi.org/10.1117/12.3021275
Event: Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023), 2023, Changchun, China

Abstract

To address the problem of large parameter and computation complexity of feature extraction network in correlation filter based target tracking algorithm, a lightweight correlation filter target tracking algorithm Iterative Enhanced Ghost Network （IEGN）IEGN is proposed. First, GhostNet is used as the backbone feature extraction network, and iterative attention feature fusion is used to increase the feature's perception ability of context. Second, the extracted deep features and handcrafted features are windowed and interpolated, and then convolved with the current filter in the Fourier domain for localization calculation. Finally, the conjugate gradient algorithm is used to optimize the loss function of the sum of response error and penalty term, and realize the filter update. Through experiments, it is concluded that the tracking speed is increased by 25% when there is GPU, and 60% when there is no GPU. The test tracking obtains 0.530 tracking accuracy, 0.362 robustness and 0.244EAO, which has the best tracking effect among the mainstream correlation filter algorithms in comparison. Compared with ResNet-50 network and VGG-16 network, this method reduces the feature parameter extraction amount by 80% and 96.4%, respectively.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xingyu Mu, Siwei Wang, Hui Chen, Xinjing Zhang, Xin Xu, and Yunfeng Li "Lightweight correlation filter based object tracking network", Proc. SPIE 13063, Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023), 130631Z (19 February 2024); https://doi.org/10.1117/12.3021275

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