Online 3D behavioral tracking of aquatic model organism with a dual-camera system
Behavioral tracking system of aquatic model organism is crucial for applications in aquaculture, environment and biomedicine, as it facilitates human to monitor subject states by automatically recognizing individual identities, and quantify their movement trajectories. Previous research has been dev...
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| Published in | Advanced engineering informatics Vol. 61; p. 102481 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.08.2024
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1474-0346 |
| DOI | 10.1016/j.aei.2024.102481 |
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| Abstract | Behavioral tracking system of aquatic model organism is crucial for applications in aquaculture, environment and biomedicine, as it facilitates human to monitor subject states by automatically recognizing individual identities, and quantify their movement trajectories. Previous research has been devoted to this topic, but they are still not simple and effective enough. Therefore, this work introduces a novel online monitoring system implemented by dual-camera equipment and software modules consisting of an object detector and a multi-view multi-target tracker. The tracker provides the abilities of cross-view matching, underwater 3D reconstruction, and 3D target tracking. Specifically, our solution adopts a new paradigm, called tracking by early-reconstruction, which prioritizes the 3D reconstruction of targets’ coordinates on a frame-by-frame basis and then tracks them directly in 3D space rather than in a 2D image plane. This paradigm simplifies the complex multi-view tracking problem into a series of local association procedures, allowing us to achieve an online resolution through the iterative approach. To verify the effectiveness of the system, we employ zebrafish as the research subject, and evaluate the accuracy and robustness of the system on tracking benchmark, behavioral tasks and simulated data. Finally, we conducted extensive experiments and demonstrated the efficiency and effectiveness of the proposed system. |
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| AbstractList | Behavioral tracking system of aquatic model organism is crucial for applications in aquaculture, environment and biomedicine, as it facilitates human to monitor subject states by automatically recognizing individual identities, and quantify their movement trajectories. Previous research has been devoted to this topic, but they are still not simple and effective enough. Therefore, this work introduces a novel online monitoring system implemented by dual-camera equipment and software modules consisting of an object detector and a multi-view multi-target tracker. The tracker provides the abilities of cross-view matching, underwater 3D reconstruction, and 3D target tracking. Specifically, our solution adopts a new paradigm, called tracking by early-reconstruction, which prioritizes the 3D reconstruction of targets’ coordinates on a frame-by-frame basis and then tracks them directly in 3D space rather than in a 2D image plane. This paradigm simplifies the complex multi-view tracking problem into a series of local association procedures, allowing us to achieve an online resolution through the iterative approach. To verify the effectiveness of the system, we employ zebrafish as the research subject, and evaluate the accuracy and robustness of the system on tracking benchmark, behavioral tasks and simulated data. Finally, we conducted extensive experiments and demonstrated the efficiency and effectiveness of the proposed system. |
| ArticleNumber | 102481 |
| Author | Xiong, Zhang Wu, Zewei Ke, Wei Zhang, Wei Sun, Guodong Wang, Cui |
| Author_xml | – sequence: 1 givenname: Zewei orcidid: 0000-0003-2289-0799 surname: Wu fullname: Wu, Zewei organization: Macao Polytechnic University, Macao 999078, China – sequence: 2 givenname: Cui orcidid: 0000-0001-9601-7824 surname: Wang fullname: Wang, Cui email: cui.wang@mpu.edu.mo organization: Macao Polytechnic University, Macao 999078, China – sequence: 3 givenname: Wei orcidid: 0000-0002-3942-0593 surname: Zhang fullname: Zhang, Wei organization: Macao Polytechnic University, Macao 999078, China – sequence: 4 givenname: Guodong orcidid: 0000-0003-3739-2792 surname: Sun fullname: Sun, Guodong organization: Beijing Forestry University, Beijing 100083, China – sequence: 5 givenname: Wei orcidid: 0000-0003-0952-0961 surname: Ke fullname: Ke, Wei organization: Macao Polytechnic University, Macao 999078, China – sequence: 6 givenname: Zhang orcidid: 0000-0002-9421-1014 surname: Xiong fullname: Xiong, Zhang organization: Beihang University, Beijing 100191, China |
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| Keywords | Deep representation learning 3D trajectories reconstruction Online monitoring system Laboratory zebrafish tracking |
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