Vehicle Detection through Traffic Video in Congested Traffic Flow
Meng Mao, Yong Zhang, Boyue Wang, Hao Liu, Baocai Yin · 2016
With the development of Intelligent Transportation Systems, it is more important to improve the calculation accuracy of traffic flow, speed and occupancy through traffic surveillance videos. The biggest challenge to calculate these parameters is getting accurate results in congested traffic situations. We have proposed a method to solve this problem, which consists of three parts. First of all, a dynamic background subtraction method is used. Secondly, we use blob tracking and module cluster or splitting to imitate vehicle contour in target tracking. Finally, we use improved three-frame difference method to calculate vehicle speed. Experimental results show that the proposed methods are superior to other traditional methods, especially in congested traffic situations.