Proceedings ArticleDOI
Traffic Monitoring and Vehicle Tracking using Roadside Cameras
Yao Jan Wu,Feng-Li Lian,Tang-Hsien Chang +2 more
- Vol. 6, pp 4631-4636
TLDR
The experimental results show that this system is capable of successfully extracting the traffic parameters, including the trajectory of the moving vehicles based on the image sequences captured by a digital camera on a free flow traffic in the daytime.Abstract:
This paper studies the integration and implementation of digital image processing techniques on the roadside camera for traffic monitoring and vehicle tracking. The image processing framework developed in this study is mainly composed of five stages: (1) pre-processing, (2) foreground segmentation, (3) shadow removal, (4) tracking, and (5) traffic parameters extraction. During the pre-processing stage, the information of road geometry is obtained and the camera is calibrated. At the foreground segmentation stage and shadow removal stage, moving vehicles are segmented from the original input images. To make the system more robust, an alpha-beta filter is used at the multi-vehicle tracking stage. Subsequently, related traffic parameters are extracted at the end of each tracking mechanism. The experimental results show that this system is capable of successfully extracting the traffic parameters, including the trajectory of the moving vehicles based on the image sequences captured by a digital camera on a free flow traffic in the daytime..read more
Citations
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A 64 $\,\times\,$ 64 CMOS Image Sensor With On-Chip Moving Object Detection and Localization
TL;DR: A 64×64 CMOS image sensor with on-chip moving object detection and localization capability, with Pixel-level storage elements (capacitors) enable the sensor to simultaneously output two consecutive frames, with temporal differences digitalized into binary events by a global differentiator.
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Image Processing Based Intelligent Traffic Controller
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Road traffic density estimation and congestion detection with a hybrid observer-based strategy
TL;DR: Results show the efficacy of the proposed HO-based EWMA-GLR method to monitor traffic congestions, and the proposed approach is compared to that of the conventional Shewhart and EWMA approaches and found better performance.
Journal ArticleDOI
An Automatic Lane Marking Detection Method With Low-Density Roadside LiDAR Data
TL;DR: In this article, an algorithm for lane detection applied to low-density roadside LiDAR is proposed, which includes three steps: ground recognition, lane marking point extraction, and pavement lane marking points clustering.
Patent
Lane level traffic
TL;DR: In this article, a controller aligns a three-dimensional map with a traffic camera view, and identifies multiple lanes in the camera view based on lane delineations of the 3D map.
References
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