A two-stage approach for front-view vehicle detection and counting at nighttime
1 Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam
2 School of Computer Science and Engineering, Water Resources University, Hanoi, Vietnam
In this paper, we introduce an approach to car detection and counting using a two-stage method of car dectection and counting. For car hypothesis, we propose a method of headlight candidate extraction using k-means clustering based segmentation which is used as a multi-thresholding method to segment the gray-image of the traffic scene with the lowest level and highest level of intensities included in seed configuration. In verification stage, both individual and paired headlights are tracked during their existence in the ROI. Kalman filter is used to track the detected headlights and counting decision is given when the pairs of headlights follow specified counting rules. The experiments are evaluated on real world traffic videos at the resolution of 640×480, 15fps. The method is robust with various situations of illumination at nighttime.
© Owned by the authors, published by EDP Sciences, 2016
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