A ship target tracking algorithm based on deep learning and multiple features

Yongmei Zhang Sr.; Jie Shu Sr.; Lei Hu Sr.; Qi Zhou Sr.; Zhirong Du Sr.

doi:10.1117/12.2559945

31 January 2020 A ship target tracking algorithm based on deep learning and multiple features

Yongmei Zhang Sr., Jie Shu Sr., Lei Hu Sr., Qi Zhou Sr., Zhirong Du Sr.

Proceedings Volume 11433, Twelfth International Conference on Machine Vision (ICMV 2019); 1143304 (2020) https://doi.org/10.1117/12.2559945
Event: Twelfth International Conference on Machine Vision, 2019, Amsterdam, Netherlands

Abstract

In videos, the waves, floating objects on the sea, peaks, and other objects passing by the ships may cause the shielding of the interest objects, and the ships are often disturbed by the same color background, which will easily lead to tracking failure. This paper presents a ship tracking algorithm based on deep learning and multi-feature, the algorithm utilizes an improved YOLO and multi-feature ship detection method to detect the ships, establishes the correlation of the same ships among different frames by the improved SIFT matching algorithm to realize ship tracking. The improved YOLO and multi-feature ship detection algorithm is proposed, YOLO method is optimized, and the optimization method is combined with HOG and LBP features, which is beneficial to solve the problems of easy omission and inaccurate positioning of YOLO network detection. SIFT matching algorithm is improved to solve the problems of lower accuracy and too long time for traditional SIFT matching algorithm, the SIFT features are reduced by MDS(multi-dimensional scaling), RANSAC(random sample consensus) is used to optimize SIFT feature matching and effectively eliminate mismatching. The experiment results show the tracking algorithm has higher accuracy, stronger robustness and better real-time.

Citation Download Citation

Yongmei Zhang Sr., Jie Shu Sr., Lei Hu Sr., Qi Zhou Sr., and Zhirong Du Sr. "A ship target tracking algorithm based on deep learning and multiple features", Proc. SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 1143304 (31 January 2020); https://doi.org/10.1117/12.2559945

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