Authors: Yucai Zhou; Zhimin Lv; Yuelin Li; Jiangchun Mo
Addresses: School of Energy and Power, Changsha University of Science and Technology, Changsha Hunan Province, 410076, China; Key Lab of Intelligent road and Vehicles Networking of Hunan Province, Changsha, Hunan, 410076, China ' Puyang Vocational and Technical College, China ' School of Automobile and Mechanical Engineering, Changsha University of Science and Technology, Changsha, Hunan, China ' Key Lab of Intelligent road and Vehicles Networking of Hunan Province, Changsha, Hunan, 410076, China
Abstract: In order to solve the problem that vehicle detection rate can be affected in complex scenarios, authors put forward the adaptive method based on GMM which sets up and updates the background and use the average neighbourhood based on HSV fast shadow elimination algorithm which improves the speed of shadow elimination. For occluded vehicles, authors use the recognition algorithm based on Kalman Filter which blocks vehicle identification, then authors adopts pyramid hierarchical search algorithm based on the template matching which segments the occluded vehicles. The experimental results show that the algorithm is simple and effective and the detection rate of vehicle is 97%, which meets the requirements of vehicle detection completely.
Keywords: vehicle occlusion; GMM; shadow removal; Kalman Filter; vehicle detection.
International Journal of Information and Communication Technology, 2020 Vol.17 No.1, pp.22 - 36
Received: 14 Nov 2018
Accepted: 08 May 2019
Published online: 07 Jul 2020 *