A Ship Target Tracking Algorithm Based on Deep Learning and Multiple Features

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.