Intensity and edge-based symmetry detection with an application to car-following
CVGIP: Image Understanding
On-Road Vehicle Detection: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detection and classification of vehicles
IEEE Transactions on Intelligent Transportation Systems
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The performance of existing vehicle detection algorithms are subject to many influences of environment, such as different lighting and weather conditions, moving vehicle shadows, etc. To solve these problems, a novel vehicle detection algorithm was proposed. Different from traditional methods, which use motion features to detect vehicles, the proposed method uses a representation of red colors to find the rear-lights regions of vehicles and uses symmetry measure function to analysis the symmetry of the color distribution, and figures out the accurate position of the symmetry axis. Then, the pair-edges are defined to reconstruct the integrated vehicle edges. Finally, a simple bounding box is fit to detected vehicle regions; the result is the location and dimensions of vehicles in the video. Experimental results show that the proposed method can be accurate and robust in detecting vehicles under different weather and lighting conditions, even at night. This method can also be used in many related applications, such as self-guided vehicles and driver assistance systems.