Multi-scale underwater object tracking by adaptive feature fusion

Ying Lu; Huibin Wang; Zhe Chen; Zhen Zhang

doi:10.1117/12.2605096

28 October 2021 Multi-scale underwater object tracking by adaptive feature fusion

Ying Lu, Huibin Wang, Zhe Chen, Zhen Zhang

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Proceedings Volume 11884, International Symposium on Artificial Intelligence and Robotics 2021; 1188412 (2021) https://doi.org/10.1117/12.2605096
Event: International Symposium on Artificial Intelligence and Robotics 2021, 2021, Fukuoka, Japan

Abstract

Different from object tacking on the ground, underwater object tracking is challenging due to the image attenuation and distortion. Also, challenges are increased by the high-freedom motion of targets under water. Target rotation, scale change, and occlusion significantly degenerate the performance of various tracking methods. Aiming to solve above problems, this paper proposes a multi-scale underwater object tracking method by adaptive feature fusion. The gray, HOG (Histogram of Oriented Gradient) and CN (Color Names) features are adaptively fused in the background-aware correlation filter (BACF) model. Moreover, a novel scale estimation method and a high-confidence model update strategy are proposed to comprehensively solve the problems caused by the scale changes and background noise influences. Experimental results demonstrate that the success ratio of the AUC criterion is 64.1% that is better than classic BACF and other methods, especially in challenging conditions.

Citation Download Citation

Ying Lu, Huibin Wang, Zhe Chen, and Zhen Zhang "Multi-scale underwater object tracking by adaptive feature fusion", Proc. SPIE 11884, International Symposium on Artificial Intelligence and Robotics 2021, 1188412 (28 October 2021); https://doi.org/10.1117/12.2605096

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KEYWORDS

Submerged target modeling

Performance modeling

Image filtering

Data modeling

RGB color model

Process modeling

Databases

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