Object identification-based stall detection and stall legitimacy analysis for traffic patterns.

Saeed, Sana; Bilal, Kashif; Rehman, Zia Ur; Azmat, Shoaib; Shuja, Junaid; Jamil, Akhtar

Illegal short-term vehicle stalls are frequent in developing countries, which disturb smooth traffic flow and results in congestion, road blockage, and even lead to accidents. Short-term stalls span a few seconds—e.g., a vehicle stopping to make way for people crossing the road. However, traditional stall detection techniques detect stalls that span 10 s or more. Moreover, existing techniques, such as background subtraction algorithms, are vulnerable to illumination changes and indicate too many false stalls with smaller stopping criteria. Furthermore, for violation indication, post-stall analysis is required to filter out the legitimate stalls where a vehicle stops for some valid reason. We present a two-fold methodology to detect illegally parked vehicles. An object identification-based technique is employed over stationary images to detect stall. Furthermore, the legitimacy of the stall is determined by performing pedestrian detection and its position analysis. We tested our proposed approach using publicly available advanced video and signal based surveillance 2007 PV dataset and ViSOR dataset. The technique successfully detects stalls with a recall of 0.96, after weaving the stopping criterion from the ground truth.

TRAFFIC patterns; VIDEO signals; TRAFFIC flow; ROAD closures; TRAFFIC monitoring; DEVELOPING countries; IDENTIFICATION

Journal of Electronic Imaging, 2022, Vol 31, Issue 6, p61812

1017-9909

Academic Journal

10.1117/1.JEI.31.6.061812