Tracking Live Fish From Low-Contrast and Low-Frame-Rate Stereo Videos

Nonextractive fish abundance estimation with the aid of visual analysis has drawn increasing attention. Unstable illumination, ubiquitous noise, and low-frame-rate (LFR) video capturing in the underwater environment, however, make conventional tracking methods unreliable. In this paper, we present a multiple fish-tracking system for low-contrast and LFR stereo videos with the use of a trawl-based underwater camera system. An automatic fish segmentation algorithm overcomes the low-contrast issues by adopting a histogram backprojection approach on double local-thresholded images to ensure an accurate segmentation on the fish shape boundaries. Built upon a reliable feature-based object matching method, a multiple-target tracking algorithm via a modified Viterbi data association is proposed to overcome the poor motion continuity and frequent entrance/exit of fish targets under LFR scenarios. In addition, a computationally efficient block-matching approach performs successful stereo matching that enables an automatic fish-body tail compensation to greatly reduce segmentation error and allows for an accurate fish length measurement. Experimental results show that an effective and reliable tracking performance for multiple live fish with underwater stereo cameras is achieved.