Parallel Fish School Tracking Based on Multiple Appearance Feature Detection

doi:10.3390/s21103476

YIC-IR

	Parallel Fish School Tracking Based on Multiple Appearance Feature Detection
	Wang, Zhitao 1,2; Xia, Chunlei2 ; Lee, Jangmyung 1
发表期刊	SENSORS
	2021-05-01
卷号	21 期号:10 页码:16
关键词	zebrafish SORT Kalman filter shape index clustering
DOI	10.3390/s21103476
通讯作者	Lee, Jangmyung(jmlee@pusan.ac.kr)
英文摘要	A parallel fish school tracking based on multiple-feature fish detection has been proposed in this paper to obtain accurate movement trajectories of a large number of zebrafish. Zebrafish are widely adapted in many fields as an excellent model organism. Due to the non-rigid body, similar appearance, rapid transition, and frequent occlusions, vision-based behavioral monitoring is still a challenge. A multiple appearance feature based fish detection scheme was developed by examining the fish head and center of the fish body based on shape index features. The proposed fish detection has the advantage of locating individual fishes from occlusions and estimating their motion states, which could ensure the stability of tracking multiple fishes. Moreover, a parallel tracking scheme was developed based on the SORT framework by fusing multiple features of individual fish and motion states. The proposed method was evaluated in seven video clips taken under different conditions. These videos contained various scales of fishes, different arena sizes, different frame rates, and various image resolutions. The maximal number of tracking targets reached 100 individuals. The correct tracking ratio was 98.60% to 99.86%, and the correct identification ratio ranged from 97.73% to 100%. The experimental results demonstrate that the proposed method is superior to advanced deep learning-based methods. Nevertheless, this method has real-time tracking ability, which can acquire online trajectory data without high-cost hardware configuration.
资助机构	National Research Foundation of Korea (NRF) - Korea government (MSIT) ; Key Research and Development Program of Yantai ; Shandong Province Key R&D Program (Major Science and Technology Innovation Project)
收录类别	SCI
语种	英语
关键词[WOS]	BEHAVIOR
研究领域[WOS]	Chemistry ; Engineering ; Instruments & Instrumentation
WOS记录号	WOS:000662651900001
引用统计	被引频次：4[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.yic.ac.cn/handle/133337/34769
专题	中国科学院烟台海岸带研究所
通讯作者	Lee, Jangmyung
作者单位	1.Pusan Natl Univ, Dept Elect Engn, Busan 46241, South Korea 2.Chinese Acad Sci, Yantai Inst Coastal Zone Res, Yantai 264003, Peoples R China
推荐引用方式 GB/T 7714	Wang, Zhitao,Xia, Chunlei,Lee, Jangmyung. Parallel Fish School Tracking Based on Multiple Appearance Feature Detection[J]. SENSORS,2021,21(10):16.
APA	Wang, Zhitao,Xia, Chunlei,&Lee, Jangmyung.(2021).Parallel Fish School Tracking Based on Multiple Appearance Feature Detection.SENSORS,21(10),16.
MLA	Wang, Zhitao,et al."Parallel Fish School Tracking Based on Multiple Appearance Feature Detection".SENSORS 21.10(2021):16.