TY  - JOUR
PY  - 2021//
TI  - Traffic foreground detection at complex urban intersections using a novel background dictionary learning model
JO  - Journal of advanced transportation
A1  - Cao, Qianxia
A1  - Wang, Zhengwu
A1  - Long, Kejun
A1  - Chen, Xinqiang
SP  - e3515512
EP  - e3515512
VL  - 2021
IS  - 
N2  - In complex urban intersection scenarios, due to heavy traffic and signal control, there are many slow-moving or temporarily stopped vehicles behind the stop lines. At these intersections, it is difficult to extract traffic parameters, such as delay and queue length, based on vehicle detection and tracking due to the dense and severe occlusion of vehicles. In this study, a novel background subtraction algorithm based on sparse representation is proposed to detect the traffic foreground at complex intersections to obtain traffic parameters. By establishing a novel background dictionary update model, the proposed method solves the problem that the background is easily contaminated by slow-moving or temporarily stopped vehicles and therefore cannot obtain the complete traffic foreground. Using the real-world urban traffic videos and the PV video sequences of <inline-formula><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mi>i</mml:mi></mml:math></inline-formula>-LIDS, we first compare the proposed method with other detection methods based on sparse representation. Then, the proposed method is compared with other commonly used traffic foreground detection models in different urban intersection traffic scenarios. The experimental results show that the proposed method performs well in keeping the background model being unpolluted from slow-moving or temporarily stopped vehicles and has a good performance in both qualitative and quantitative evaluations.<p />  <p>Language: en</p>
LA  - en
SN  - 0197-6729
UR  - http://dx.doi.org/10.1155/2021/3515512
ID  - ref1
ER  -