Object tracking algorithm with multi-scale channel attention

Caixia Shu; Rui Li; Jian Chen

doi:10.1117/12.2659092

23 November 2022 Object tracking algorithm with multi-scale channel attention

Caixia Shu, Rui Li, Jian Chen

Proceedings Volume 12454, International Symposium on Robotics, Artificial Intelligence, and Information Engineering (RAIIE 2022); 124542H (2022) https://doi.org/10.1117/12.2659092
Event: International Symposium on Robotics, Artificial Intelligence, and Information Engineering (RAIIE 2022), 2022, Hohhot, China

Abstract

In recent years, the application of Siamese networks for visual target tracking has brought a great improvement in tracker performance, allowing both accuracy and real-time performance. However, the accuracy of Siamese network trackers is largely limited. Object tracking algorithm with multi-scale channel attention is proposed based on the Siamese object tracking algorithm of fully convolutional classification and regression. Firstly, the backbone network ResNet50 is improved, which combined with multi-scale channel attention for feature extraction and enhancement. Then, adding a spatial attention mechanism to focus on information about the location features of the target image within each channel after the features are extracted from the template branch. At last, it performs fusion, classification and regression successfully. Compared with other advanced trackers, our approach achieves higher accuracy and success rate, especially in complex scenarios such as fast motion, occlusion, similarity interference, and scale changes.

Citation Download Citation

Caixia Shu, Rui Li, and Jian Chen "Object tracking algorithm with multi-scale channel attention", Proc. SPIE 12454, International Symposium on Robotics, Artificial Intelligence, and Information Engineering (RAIIE 2022), 124542H (23 November 2022); https://doi.org/10.1117/12.2659092

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KEYWORDS

Feature extraction

Optical tracking

Neural networks

Convolution

Algorithm development

Video

Visualization

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