Multiple fish tracking with an NACA airfoil model for collective behavior analysis

Kei Terayama,Hitoshi Habe,Masa-Aki Sakagami

doi:10.1186/s41074-016-0004-1

Abstract

Abstract We propose a visual tracking method with an NACA airfoil model for dense fish schools in which occlusions occur frequently. Although much progress has been made for tracking multiple objects, it remains a challenging task to track individuals due to factors such as occlusion and target appearance variation. In this paper, we first introduce a NACA airfoil model as a deformable appearance model of fish. For occluded fish, we estimate their positions, angles, and postures with template matching and simulated annealing algorithms to effectively optimize their parameters. To improve performance of tracking, we repeatedly track fish with the parameter estimation algorithm forwards and backwards. We prepared two real fish scenes in which the average number of fish is over 25 in each frame and multiple fish superimpose over 50 times. Experimental results for the scenes show that fish are practically tracked with our method compared to a tracking method based on a mixture particle filter. Over 75 % of fish in each scene have been tracked throughout the scene, and the average difference is less than 4 % of the mean body length of the school.

Highlights

The tracking of multiple fish in a tank to measure their behaviors has many important applications in various fields of natural science, such as animal behavior and neuroscience [1–3]
Videos of multiple fish pose many difficulties for visual tracking: fish frequently overlap with each other, their textures are weak, they deform their bodies by beating with their tails, and identification is difficult because they are homogeneous
3 Experimental results We conducted experiments to show the effectiveness of the proposed method

Summary

Introduction

The tracking of multiple fish in a tank to measure their behaviors has many important applications in various fields of natural science, such as animal behavior and neuroscience [1–3]. The detection of fish, i.e., counting their number and estimating their positions and directions, in a cluster of fish such as that. Terayama et al tracked multiple fish in such a dense school using their appearance model based on the images of fish in a video [11]. They showed that if the number of fish in a cluster of fish is known, their positions and other parameters can be estimated by matching all of the combinations of the possible parameters. We propose a novel multiple fish tracking method for a dense school of fish.

Appearance model of fish

Scene Method Rcll

Conclusion