Appearance-based multiple fish tracking for collective motion analysis

Abstract

We propose a visual tracking method for dense fish schools in which occlusions occur frequently. Although much progress has been made for tracking multiple objects in video images, it is challenging to track individuals in highly dense groups. For occluded fishes, estimation of their positions and directions is difficult. However, if we know the number of fishes in a local area, we can accurately estimate their states by matching all of the combinations of possible parameters on the basis of our appearance model. We apply the idea to track multiple fishes in a school. Experimental results show that multiple fishes are practically tracked with our method compared to a well-known tracking method, and the average difference is less than 4%b of the mean body length of the school.