DC-MOT: Motion Deblurring and Compensation for Multi-Object Tracking in UAV Videos
Cheng Song, Meibao Yao, Xueming Xiao
Abstract
In this paper, we propose a multi-object tracking framework for videos captured by UAVs, considering motion imperfection in the following two aspects: 1) motion blurring of objects due to high-speed motion of the UAV and the objects, deteriorating the performance of the detector; 2) motion coupling of the global movement of the UAV camera with the object motion, resulting in the nonlinearity of objects trajectories in adjacent frames and further more difficult to predict. For motion blurring, this paper proposes a hybrid deblurring module that deals with the blurred frames while retaining the clear frames, trading off between video tracking performance and spatio-temporal consistency. For motion coupling, we proposed a motion compensation module to align adjacent frames by feature matching, and the corrected target position is obtained in the next frame to alleviate the interference of camera movement with tracking. We evaluate the proposed methods on VisDrone dataset and validate that our framework achieves new state-of-the-art performance on UAV-based MOT systems.