计算机视觉
去模糊
人工智能
计算机科学
运动补偿
运动(物理)
视频跟踪
跟踪(教育)
匹配移动
运动估计
特征(语言学)
帧(网络)
运动模糊
对象(语法)
图像复原
图像处理
图像(数学)
哲学
电信
语言学
教育学
心理学
作者
Cheng Song,Meibao Yao,Xueming Xiao
标识
DOI:10.1109/icra48891.2023.10160931
摘要
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.
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