A real-time video surveillance system for traffic pre-events detection.

In this study, a conceptual framework is proposed for the development of a video surveillance-based system for improving road safety. Based on the framework, a set of algorithms are developed which are capable of detecting various traffic pre-events from traffic videos, such as speed violation, one-way traffic, overtaking, illegal parking, and wrong drop-off location of passengers. After detecting the pre-events, an alarm will be automatically generated in the control room which helps to take precautionary measures to avoid any potential mishap on road, thereby, improving the road safety. In previous studies, a single system can handle either one or two pre-events. Whereas, in our present study, five anomalies can be detected in a single system using five different algorithms. Our study further contributes to the detection of "wrong drop-off location of passengers". The effectiveness of the developed algorithms is demonstrated over 132 traffic videos acquired from an integrated plant in India. Some additional comparative studies for overtaking and illegal parking are done using two benchmark datasets, namely 'CamSeq01' and 'ISLab-PVD'. Through an extensive study, it can be concluded that our developed algorithms are superior to some state-of-the-art algorithms in the detection of pre-events on road.