Y.-L. Chen, B.-F. Wu, C.-T. Lin, C.-J. Fan, and C.-M. Hsieh
Vehicle detection, vehicle tracking, nighttime driving, image segmentation, driver assistance, autonomous vehicles
This study presents an effective method for detecting and tracking vehicles in front of a camera-assisted car during nighttime driving. The proposed method identifies vehicles based on detecting and locating vehicle headlights and taillights by using the techniques of image segmentation and pattern analysis. First, to effectively extract bright objects of interest, a fast bright object segmentation process based on automatic multilevel histogram thresholding is applied on the nighttime road-scene images. This automatic multilevel thresholding approach can provide robustness and adaptability for the detection system to be operated well under various illumination conditions at night. The extracted bright objects are processed by a spatial clustering and tracking procedure by locating and analyzing the spatial and temporal features of vehicle light patterns, and estimating the distance between the detected vehicles and the camera-assisted car. Experimental results demonstrate the feasibility and effectiveness of the proposed method for detecting and tracking vehicles at night.
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