Improving 3-D Zebrafish Tracking With Multiview Data Fusion and Global Association
Cui Wang, Zewei Wu, Yanbing Chen, Wei Zhang, Wei Ke, Xiong Zhang
Abstract
Zebrafish behavioral patterns reveal valuable insights for biomedical research. To accurately identify these patterns, visual tracking systems need to reconstruct 3-D trajectories from multiview video sequences. However, 3-D zebrafish tracking faces challenges such as the dynamics in movements, the similarity in appearances, and the distortion caused by different viewpoints. In this article, we propose a new method for robust 3-D zebrafish trajectory reconstruction based on multiview data fusion and global association. Our method generates reliable segments of 2-D/3-D trajectories, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tracklets</i> , where we consider short-term cues of appearance similarity and motion consistency and propose corresponding scoring metrics. Moreover, we use a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">lazy-reconstruction</i> strategy to enhance the overall accuracy of 3-D trajectories by taking into account the global context. Extensive experiments on the public 3D-ZeF20 dataset demonstrate the effectiveness of the proposed method, achieving 67.9% multiple object tracking accuracy (MOTA), 64.3% ID F1 Score (IDF1), and 55.0 MTBFm.