融合运动信息与表观信息的多目标跟踪算法

Multi-target Tracking Algorithm by Combining Motion Information and Apparent Information

多目标跟踪是计算机视觉领域的重要研究方向,其在智能视频监控、人机交互、机器人导航、公共安全等领域有着重要的作用。目前目标跟踪算法仍面临诸多的挑战,例如遮挡、背景复杂、运动模糊等因素所造成的影响难以完全规避。文中基于一种简单的在线跟踪方法,提出一种融合多类信息的算法,有效地提升了跟踪器的性能。模型关注于帧与帧之间的目标检测与数据关联问题,依赖于不同帧之间目标运动与表观的相似性,当目标丢失及存在遮挡时,融合多源信息减少相关的不确定性。同时,该算法在真实环境中可实现实时跟踪的性能。实验评估结果表明,提出的跟踪器在公开数据集上具有良好的性能,可以显著减少目标丢失率以及身份交换率。

Multi-target tracking is an important research direction in the field of computer vision.Multi-target tracking plays an important role in the fields of intelligent video surveillance,human-computer interaction,robot navigation,and public safety.At present,the target tracking algorithm still faces many challenges,such as the effects of occlusion,complex background,motion blur,etc.,which are difficult to completely avoid.This paper proposes an algorithm that combines multiple types of information,which effectively improves the performance of the tracker.The model focuses on the problem of object detection and data association between frames,which depends on the similarity of the target motion and the apparent between different frames.When the target is lost and there is occlusion,the fusion of multi-source information reduces the related uncertainty.At the same time,the algorithm achieves real-time tracking performance in real-world environments.Experimental evaluation shows that the proposed tracker has good performance on the public data set,greatly reducing the target loss rate and ID switch.