NDLI: Front-view car detection and counting with occlusion in dense traffic flow

Content Provider	Springer Nature Link
Author	Pham, Huy Lee, Byung Ryong
Copyright Year	2015
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.
Starting Page	1150
Ending Page	1160
Page Count	11
File Format	PDF
ISSN	15986446
Journal	International Journal of Control, Automation and Systems
Volume Number	13
Issue Number	5
e-ISSN	20054092
Language	English
Publisher	Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers
Publisher Date	2015-07-14
Publisher Place	Bucheon, Seoul
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Car counting HOG Hough transform tracking traffic monitoring vehicle detection Control, Robotics, Mechatronics
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Computer Science Applications

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