Roadside Cross-Camera Vehicle Tracking Combining Visual and Spatial-Temporal Information for a Cloud Control System

Bolin Gao;Zhuxin Li;Dong Zhang;Yanwei Liu;Jiaxing Chen;Ziyuan Lv
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引用次数: 0

Abstract

Roadside cameras play a crucial role in road traffic, serving as an indispensable part of integrated vehicle-road-cloud systems due to their extensive visibility and monitoring capabilities. Nevertheless, these cameras face challenges in continuously tracking targets across perception domains. To address the issue of tracking vehicles across nonoverlapping perception domains between cameras, we propose a cross-camera vehicle tracking method within a Vehicle-Road-Cloud system that integrates visual and spatiotemporal information. A Gaussian model with microlevel traffic features is trained using vehicle information obtained through online tracking. Finally, the association of vehicle targets is achieved through the Gaussian model combining time and visual feature information. The experimental results indicate that the proposed system demonstrates excellent performance.
结合视觉和时空信息的路边跨摄像头车辆跟踪,用于云控制系统
路边摄像头在道路交通中发挥着至关重要的作用,凭借其广泛的可视性和监控能力,成为车-路-云集成系统不可或缺的一部分。然而,这些摄像头在跨感知域持续跟踪目标方面面临挑战。为了解决跨摄像机非重叠感知域追踪车辆的问题,我们在车路云系统中提出了一种跨摄像机车辆追踪方法,该方法整合了视觉和时空信息。利用在线跟踪获得的车辆信息,训练出具有微观交通特征的高斯模型。最后,通过结合时间和视觉特征信息的高斯模型实现车辆目标的关联。实验结果表明,所提出的系统性能卓越。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
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