Vehicle trajectory extraction and integration from multi-direction video on urban intersection

IF 3.7 2区 工程技术 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Jinjun Tang, Weihe Wang
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引用次数: 0

Abstract

With the gradual maturity of computer vision technology, using intersection surveillance videos for vehicle trajectory extraction has become a popular method to analyze vehicle conflicts and safety in urban intersection. However, many intersection surveillance videos have blind spots, failing to fully cover the entire intersection. Vehicles may also obstruct each other, resulting in incomplete vehicle trajectories. The angle of surveillance videos can also lead to inaccurate trajectory extraction. In response to these challenges, this study proposes an vehicle trajectory extraction and integration framework using surveillance videos collected from four entrance of urban intersection. The framework first employs the improved YOLOv5s model to detect the positions of vehicles. Then, we proposed an object tracking model MS-SORT to extract the trajectories in each surveillance video. Subsequently, the trajectories of each surveillance video are mapped into the same coordinate system. Then the integration of trajectories is achieved using space–time information and re-identification (ReID) methods. The framework extracts and integrates trajectories from four intersection surveillance videos, obtaining trajectories with significantly broader temporal and spatial coverage compared to those obtained from any single direction of surveillance video. Our detection model improved mAP by 1.3 percentage points compared to the basic YOLOv5s, and our object tracking model improved MOTA and IDF1 by 2.6 and 2.1 percentage points compared to DeepSORT. The trojectory integration method achieved 94.7 % of F1-Score and RMSE of 0.51 m. The average length and number of the extracted trajectories has increased by at least 47.6 % and 24.2 % respectively compared to trajectories extracted from a single video.

从城市交叉路口的多向视频中提取和整合车辆轨迹
随着计算机视觉技术的逐渐成熟,利用十字路口监控视频进行车辆轨迹提取已成为分析城市十字路口车辆冲突和安全的常用方法。然而,许多交叉路口监控视频存在盲区,无法完全覆盖整个交叉路口。车辆之间也可能相互遮挡,导致车辆轨迹不完整。监控视频的角度也会导致轨迹提取不准确。为应对这些挑战,本研究利用从城市十字路口四个入口采集的监控视频,提出了一种车辆轨迹提取和整合框架。该框架首先采用改进的 YOLOv5s 模型来检测车辆的位置。然后,我们提出了一个物体跟踪模型 MS-SORT,以提取每个监控视频中的轨迹。随后,将每个监控视频的轨迹映射到同一坐标系中。然后使用时空信息和重新识别(ReID)方法实现轨迹整合。该框架从四个交叉路口的监控视频中提取并整合轨迹,获得的轨迹与从任何单一方向的监控视频中获得的轨迹相比,在时间和空间覆盖范围上都明显更广。与基本的 YOLOv5s 相比,我们的检测模型将 mAP 提高了 1.3 个百分点;与 DeepSORT 相比,我们的目标跟踪模型将 MOTA 和 IDF1 提高了 2.6 和 2.1 个百分点。与从单个视频中提取的轨迹相比,提取轨迹的平均长度和数量至少分别增加了 47.6% 和 24.2%。
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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来源期刊
Displays
Displays 工程技术-工程:电子与电气
CiteScore
4.60
自引率
25.60%
发文量
138
审稿时长
92 days
期刊介绍: Displays is the international journal covering the research and development of display technology, its effective presentation and perception of information, and applications and systems including display-human interface. Technical papers on practical developments in Displays technology provide an effective channel to promote greater understanding and cross-fertilization across the diverse disciplines of the Displays community. Original research papers solving ergonomics issues at the display-human interface advance effective presentation of information. Tutorial papers covering fundamentals intended for display technologies and human factor engineers new to the field will also occasionally featured.
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