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Real time vehicle detection and tracking on multiple lanes

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Bibliographic Details
Title: Real time vehicle detection and tracking on multiple lanes
Authors: Ivanjko, Edouard, Gold, Hrvoje
Publisher Information: 2014.
Publication Year: 2014
Subject Terms: Multiple object detection, vehicle detection, trajectory estimation, intelligent transportation system (ITS), algorithm parallelization, vehicle tracking
Description: Development of computing power and cheap video cameras enabled today’s traffic management systems to include more cameras and computer vision applications for transportation system monitoring and control. Combined with image processing algorithms cameras are used as sensors to measure road traffic parameters like flow, origindestination matrices, classify vehicles, etc. In this paper development of a system capable to measure traffic flow and estimate vehicle trajectories on multiple lanes using only one static camera is described. Vehicles are detected as moving objects using foreground and background image segmentation. Adjacent pixels in the moving objects image are grouped together and a weight factor based on cluster area, cluster overlapping area and distance between multiple clusters is computed to enable multiple moving object tracking. To ensure real time capabilities, image processing algorithm computation distribution between CPU and GPU is applied. Described system is tested using real traffic video footage obtained from Croatian highways.
Document Type: Conference object
Access URL: http://wscg.zcu.cz/WSCG2014/!!_2014-Posters-Proceedings.pdf
Accession Number: edsair.dris...01492..b0ef3d9054cc9766c82d35f4cb9b0880
Database: OpenAIRE
Description
Abstract:Development of computing power and cheap video cameras enabled today’s traffic management systems to include more cameras and computer vision applications for transportation system monitoring and control. Combined with image processing algorithms cameras are used as sensors to measure road traffic parameters like flow, origindestination matrices, classify vehicles, etc. In this paper development of a system capable to measure traffic flow and estimate vehicle trajectories on multiple lanes using only one static camera is described. Vehicles are detected as moving objects using foreground and background image segmentation. Adjacent pixels in the moving objects image are grouped together and a weight factor based on cluster area, cluster overlapping area and distance between multiple clusters is computed to enable multiple moving object tracking. To ensure real time capabilities, image processing algorithm computation distribution between CPU and GPU is applied. Described system is tested using real traffic video footage obtained from Croatian highways.