A lightweight convolutional network for infrared object tracking

Qiang Fan; ZhiJie Zhang; Yue Yang

doi:10.1117/12.2611696

15 February 2022 A lightweight convolutional network for infrared object tracking

Qiang Fan, ZhiJie Zhang, Yue Yang

Proceedings Volume 12166, Seventh Asia Pacific Conference on Optics Manufacture and 2021 International Forum of Young Scientists on Advanced Optical Manufacturing (APCOM and YSAOM 2021); 121661G (2022) https://doi.org/10.1117/12.2611696
Event: Seventh Asia Pacific Conference on Optics Manufacture and 2021 International Forum of Young Scientists on Advanced Optical Manufacturing (APCOM and YSAOM 2021), 2021, Hong Kong, Hong Kong

Abstract

As an important application of computer vision, visual tracking has being a fundamental topic. Compared with visible image, infrared image has the characteristic of low resolution, blurred contour and single color feature. Thus, it is still a challenge for infrared object tracking. Further, it is difficult to balance the real-time performance and accuracy. This paper proposed a tracker based on the SiamRPN tracker with a deeper and lightweight MobileNet V2 structure as the backbone network. During network training, the weights are updated by the scheme of the model-independent metalearning method. The computed model only passes a few gradient descents on the first frame can obtains the most suitable for the current frame. In the end, the tracker is tested on various datasets. Experimental results show that the tracker can achieve a balance between tracking accuracy and inference speed, which is crucial for deployment on mobile devices.

Citation Download Citation

Qiang Fan, ZhiJie Zhang, and Yue Yang "A lightweight convolutional network for infrared object tracking", Proc. SPIE 12166, Seventh Asia Pacific Conference on Optics Manufacture and 2021 International Forum of Young Scientists on Advanced Optical Manufacturing (APCOM and YSAOM 2021), 121661G (15 February 2022); https://doi.org/10.1117/12.2611696

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