An effective and robust method for tracking multiple fish in video image based on fish head detection

BMC Bioinformatics

Methodology Article

Shuo Hong Wang¹  Yan Qiu Chen¹  Xi En Cheng¹  Zhi-Ming Qian² 

[1] School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China;School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China;Chuxiong Normal University, Chuxiong, China;

关键词: Fish detection;    Fish tracking;    Global optimization;    Occlusion;   

DOI  :  10.1186/s12859-016-1138-y

 received in 2015-05-31, accepted in 2016-06-13,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundFish tracking is an important step for video based analysis of fish behavior. Due to severe body deformation and mutual occlusion of multiple swimming fish, accurate and robust fish tracking from video image sequence is a highly challenging problem. The current tracking methods based on motion information are not accurate and robust enough to track the waving body and handle occlusion. In order to better overcome these problems, we propose a multiple fish tracking method based on fish head detection.ResultsThe shape and gray scale characteristics of the fish image are employed to locate the fish head position. For each detected fish head, we utilize the gray distribution of the head region to estimate the fish head direction. Both the position and direction information from fish detection are then combined to build a cost function of fish swimming. Based on the cost function, global optimization method can be applied to associate the target between consecutive frames. Results show that our method can accurately detect the position and direction information of fish head, and has a good tracking performance for dozens of fish.ConclusionThe proposed method can successfully obtain the motion trajectories for dozens of fish so as to provide more precise data to accommodate systematic analysis of fish behavior.

【 授权许可】

CC BY   
© The Author(s). 2016