Robust multi-scale ship tracking via multiple compressed features fusion

• Published in: Signal processing. Image communication Vol. 31; pp. 76 - 85
• Main Authors: Teng, Fei, Liu, Qing
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.02.2015
• Subjects: Algorithms; Coarse-to-fine strategy; Compressed; Compressive sensing theory; Feature extraction; Frames; Image detection; Inland waterways; Naive Bayes classifier; Random measurement matrices; Ships; Tracking (position); Compressive sensing theory; Coarse-to-fine strategy; Naive Bayes classifier; Random measurement matrices
• ISSN: 0923-5965; 1879-2677
• DOI: 10.1016/j.image.2014.12.006

In this paper, we address the problem of tracking a single ship in inland waterway closed circuit television (CCTV) video sequences given its location in the first frame and no other prior information. First, based on the compressive sensing theory, we employ two kinds of random measurement matrices to extract two complementary good features to track the target ship. Second, in order to track both location and scale, we construct our random measurement matrices according to spatial and temporal structure constraints in consecutive frames, which can be easily obtained and recorded in an offline manner. Having obtained the low-dimensional features in the compressed domain, we further take the different discriminability strengths of the extracted features into account and perform feature evaluations through their cumulative classification performances. A naive Bayes classifier with online update is employed to determine whether the image patch belongs to the foreground or background and a coarse-to-fine strategy is adopted to speed up the time-consuming detection procedure. Finally, both qualitative and quantitative evaluations on numerous challenging CCTV videos demonstrate that the proposed algorithm outperforms several state-of-the-art methods in terms of accuracy, precision and robustness •Extract two complementary good features to track the target ship.•Construct random measurement matrices according to spatio-temporal structure constraints.•Perform efficient feature evaluation through cumulative classification performances.