Intelligent Vehicles for Increasing Driving Safety
Multiple vehicle detection and tracking using ongoing vehicle shadow information
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Developing on-board driver assistance systems (DAS) requires understanding of various events involving the motions of the vehicles in the vicinity of the host vehicle. Determining the position of other vehicles on the road is a key information to help driver assistance systems. Thus, robust and reliable vehicle detection and tracking are the basic steps in these systems. Since monocular vision based systems are particularly interesting for the advantage of reducing costs and maintenance and for the high fidelity information they give about the driving environment, the problem can be addressed ...
Developing on-board driver assistance systems (DAS) requires understanding of various events involving the motions of the vehicles in the vicinity of the host vehicle. Determining the position of other vehicles on the road is a key information to help driver assistance systems. Thus, robust and reliable vehicle detection and tracking are the basic steps in these systems. Since monocular vision based systems are particularly interesting for the advantage of reducing costs and maintenance and for the high fidelity information they give about the driving environment, the problem can be addressed by applying computer vision techniques. This work has mainly been focused on detecting and tracking vehicles viewed from inside a vehicle the camera mounted in daylight conditions. The approach presented in the book uses vehicle shadow clues and vehicle edge information to obtain cost effective and fast estimation. After extracting vehicles, the algorithm effectively track them using a Kalman filter based tracking algorithm. Several sequences from real traffic situations have been tested, obtaining highly accurate multiple vehicle detections.
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