School of Intelligent Engineering, Shaoguan University, Shaoguan, 512005, China.
Foshan Nanhai Guangdong Technology University CNC Equipment Cooperative Innovation Institute, Foshan, 528225, China.
Sci Rep. 2022 Jun 30;12(1):11052. doi: 10.1038/s41598-022-15000-4.
Tracking and associating different views of the same target across moving cameras is challenging as its appearance, pose and scale may vary greatly. Moreover, with multiple targets a management module is needed for new targets entering and old targets exiting the field of view of each camera. To address these challenges, we propose DMMA, a Distributed Multi-camera Multi-target Association for real-time tracking that employs a target management module coupled with a local data-structure containing the information on the targets. The target management module shares appearance and label information for each known target for inter-camera association. DMMA is designed as a distributed target association that allows a camera to join at any time, does not require cross-camera calibration, and can deal with target appearance and disappearance. The various parts of DMMA are validated using benchmark datasets and evaluation criteria. Moreover, we introduce a new mobile-camera dataset comprising six different scenes with moving cameras and objects, where DMMA achieves 92% MCTA on average. Experimental results show that the proposed tracker achieves a good association accuracy and speed trade-off by working at 32 frames per second (fps) with high definition (HD) videos.
跨移动摄像机跟踪和关联同一目标的不同视图具有挑战性，因为其外观、姿势和比例可能会有很大的变化。此外，对于多个目标，需要一个管理模块来处理每个摄像机的视场中进入和离开的新目标和旧目标。为了解决这些挑战，我们提出了 DMMA，一种用于实时跟踪的分布式多摄像机多目标关联方法，它采用了一个目标管理模块，并结合了一个包含目标信息的本地数据结构。目标管理模块共享每个已知目标的外观和标签信息，以进行摄像机间的关联。DMMA 被设计为一种分布式目标关联方法，允许摄像机随时加入，不需要跨摄像机校准，并且可以处理目标的出现和消失。使用基准数据集和评估标准验证了 DMMA 的各个部分。此外，我们引入了一个新的移动摄像机数据集，包含六个具有移动摄像机和物体的不同场景，其中 DMMA 在平均水平上实现了 92%的 MCTA。实验结果表明，所提出的跟踪器通过以每秒 32 帧（fps）的速度和高清（HD）视频工作，实现了良好的关联准确性和速度折衷。
