Underwater Fish Tracking for Moving Cameras Based on Deformable Multiple Kernels

• Published in: IEEE transactions on systems, man, and cybernetics. Systems Vol. 47; no. 9; pp. 2467 - 2477
• Main Authors: Meng-Che Chuang, Jenq-Neng Hwang, Jian-Hui Ye, Shih-Chia Huang, Williams, Kresimir
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Aquatic environment; Automotive parts; Cameras; Color; Configurations; Deformable models; Deformable part model (DPM); Deformation; Ecological monitoring; Feature extraction; Fish; Fisheries; fisheries application; Formability; Histograms; Kernel; Kernels; mean-shift (MS) algorithm; moving cameras; Object tracking; State of the art; Target tracking; Texture; Traffic surveillance; Underwater
• ISSN: 2168-2216; 2168-2232
• DOI: 10.1109/TSMC.2016.2523943

Fishery surveys that call for the use of single or multiple underwater cameras have been an emerging technology as a nonextractive mean to estimate the abundance of fish stocks. Tracking live fish in an open aquatic environment posts challenges that are different from general pedestrian or vehicle tracking in surveillance applications. In many rough habitats, fish are monitored by cameras installed on moving platforms, where tracking is even more challenging due to inapplicability of background models. In this paper, a novel tracking algorithm based on the deformable multiple kernels is proposed to address these challenges. Inspired by the deformable part model technique, a set of kernels is defined to represent the holistic object and several parts that are arranged in a deformable configuration. Color histogram, texture histogram, and the histogram of oriented gradients (HOGs) are extracted and serve as object features. Kernel motion is efficiently estimated by the mean-shift algorithm on color and texture features to realize tracking. Furthermore, the HOG-feature deformation costs are adopted as soft constraints on kernel positions to maintain the part configuration. Experimental results on practical video set from underwater moving cameras show the reliable performance of the proposed method with much less computational cost comparing with state-of-the-art techniques.