Front-view car detection and counting with occlusion in dense traffic flow. Van Pham, H. & Lee, B. R. International Journal of Control, Automation and Systems, 13(5):1150–1160, 2015. ![Front-view car detection and counting with occlusion in dense traffic flow [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex In dense traffic flow, car occlusion is usually one of the great challenges of vehicle detection and tracking in traffic monitoring systems. Current methods of car hypothesis such as symmetry or shadow based method work only with non-occluded cars. In this paper, we proposed an approach to car detection and counting using a new method of car hypothesis based on car windshield appearance which is the most feasible cue to hypothesize cars in occlusion situations. In hypothesis stage, Hough transformation is used to detect trapezoid-like regions where a car’s windshield could be located, and then candidate car regions are estimated by the windshield region and its size. In verification stage, HOG descriptor and a well-collected dataset are used to train a linear SVM classifier for detecting cars at a high accuracy rate. Then, a tracking process based on Kalman filter is used to track the movement of detected cars in consecutive frames of traffic videos, followed by rule-based reasoning for counting decision. Experimental results on real traffic videos showed that the system is able to detect, track and count multiple cars including occlusion in dense traffic flow in real-time.
@article{Van_Huy_Pham_70120028,
title = {Front-view car detection and counting with occlusion in dense traffic flow},
volume = {13},
issn = {20054092},
url = {http://doi.org/10.1007/s12555-014-0229-7},
doi = {10.1007/s12555-014-0229-7},
abstract = {In dense traffic flow, car occlusion is usually one of the great challenges of vehicle detection and tracking in traffic monitoring systems. Current methods of car hypothesis such as symmetry or shadow based method work only with non-occluded cars. In this paper, we proposed an approach to car detection and counting using a new method of car hypothesis based on car windshield appearance which is the most feasible cue to hypothesize cars in occlusion situations. In hypothesis stage, Hough transformation is used to detect trapezoid-like regions where a car’s windshield could be located, and then candidate car regions are estimated by the windshield region and its size. In verification stage, HOG descriptor and a well-collected dataset are used to train a linear SVM classifier for detecting cars at a high accuracy rate. Then, a tracking process based on Kalman filter is used to track the movement of detected cars in consecutive frames of traffic videos, followed by rule-based reasoning for counting decision. Experimental results on real traffic videos showed that the system is able to detect, track and count multiple cars including occlusion in dense traffic flow in real-time.},
number = {5},
journal = {International Journal of Control, Automation and Systems},
author = {Van Pham, Huy and Lee, Byung Ryong},
year = {2015},
keywords = {Car counting, HOG, Hough transform, tracking, traffic monitoring, vehicle detection},
pages = {1150--1160},
}
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In hypothesis stage, Hough transformation is used to detect trapezoid-like regions where a car’s windshield could be located, and then candidate car regions are estimated by the windshield region and its size. In verification stage, HOG descriptor and a well-collected dataset are used to train a linear SVM classifier for detecting cars at a high accuracy rate. Then, a tracking process based on Kalman filter is used to track the movement of detected cars in consecutive frames of traffic videos, followed by rule-based reasoning for counting decision. 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