Underwater Fish Tracking for Moving Cameras based on Deformable Multiple Kernels

TL;DR

This paper introduces a novel deformable multiple kernels (DMK) tracking algorithm tailored for underwater fish monitoring from moving cameras, effectively handling challenging aquatic environments and deformable fish shapes.

Contribution

The proposed DMK algorithm adapts deformable part models for underwater fish tracking, combining color, texture, and HOG features for robust and efficient tracking in dynamic aquatic habitats.

Findings

Reliable performance on underwater videos

Less computational cost than state-of-the-art methods

Effective handling of deformable fish shapes

Abstract

Fishery surveys that call for the use of single or multiple underwater cameras have been an emerging technology as a non-extractive 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 (DMK) is proposed to address these challenges. Inspired by the deformable part model (DPM) 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 (HOG) 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.