Chaim Chai Elchik
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Chaim Chai Elchik has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Ali Mohammed Mansoor
Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands
1 shared publication - Fatemeh Karimi Nejadasl
Department of Theoretical and Computational Ecology Institute for Biodiversity and Ecosystem Dynamics University of Amsterdam Amsterdam The Netherlands
1 shared publication - Seyed Sahand Mohammadi Ziabari
Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands
1 shared publication
Latest Publications
- Open Access
A Framework for Multi-View Multiple Object Tracking using Single-View Multi-Object Trackers on Fish Data
Multi-object tracking (MOT) in computer vision has made significant advancements, yet tracking small fish in underwater environments presents unique challenges due to complex 3D motions and data noise. Traditional single-view MOT models often fall short in these settings. This thesis addresses these challenges by adapting state-of-the-art single-view MOT models, FairMOT and YOLOv8, for underwater fish detecting and tracking in ecological studies. The core contribution of this research is the development of a multi-view framework that utilizes stereo video inputs to enhance tracking accuracy and fish behavior pattern recognition. By integrating and evaluating these models on underwater fish video datasets, the study aims to demonstrate significant improvements in precision and reliability compared to single-view approaches. The proposed framework detects fish entities with a relative accuracy of 47% and employs stereo-matching techniques to produce a novel 3D output, providing a more comprehensive understanding of fish movements and interactions
Open AccessA Framework for Multi-View Multiple Object Tracking using Single-View Multi-Object Trackers on Fish Data
Multi-object tracking (MOT) in computer vision has made significant advancements, yet tracking small fish in underwater environments presents unique challenges due to complex 3D motions and data noise. Traditional single-view MOT models often fall short in these settings. This thesis addresses these challenges by adapting state-of-the-art single-view MOT models, FairMOT and YOLOv8, for underwater fish detecting and tracking in ecological studies. The core contribution of this research is the development of a multi-view framework that utilizes stereo video inputs to enhance tracking accuracy and fish behavior pattern recognition. By integrating and evaluating these models on underwater fish video datasets, the study aims to demonstrate significant improvements in precision and reliability compared to single-view approaches. The proposed framework detects fish entities with a relative accuracy of 47% and employs stereo-matching techniques to produce a novel 3D output, providing a more comprehensive understanding of fish movements and interactions