Reconstruction of Agents’ Corrupted Trajectories of Collective Motion Using Low-rank Matrix Completion
Kelum Gajamannage, Randy Clinton Paffenroth · 2019
Tracking of trajectories of mutually interacted collectively moving agents such as fish, birds, insects, and even humans is an active field in computer vision. However, the trajectories produced by multi-object tracking methods might consist of unconstructed segments of trajectories due to the natural phenomena such as occlusion, change of illumination, etc., which require robust tracking methods. Some tracking methods employ computationally expensive approximation schemes to connect these segments. In this work, we utilize mutual interactions and dependencies between the agents to reconstruct the missing segments of the trajectories. Due to these interactions, the coordinate matrix representing the particles' trajectories of collective motion is often low-rank. Thus, we utilize a low-rank matrix completion technique to reconstruct incomplete trajectories. We apply this approach for two representative self-propelled particle swarms, simulated by the classic Vicsek model, that imitate two real-life collective motion scenarios and use low-rank approximations to analyze their incomplete trajectories.