Event Detection on Roads Using Perceptual Video Summarization

Roads are the vital mode of transportation for people and goods around the globe and its use has grown dramatically over the years. There is one death every four minutes due to road accidents in the developing nations. This is of deep concern to the entire humanity. Road accident detection and vehicle behavior analysis is of great interest to the research community in intelligent transportation systems. It is very difficult from the state of the art techniques to provide the abstract form of salient parts of accidents from road surveillance videos. To resolve these issues, we present perceptual video summarization techniques to enrich the speed of visualizing the accident content from a stack of videos. The problem of vehicle analysis is formulated as an optimization problem. To the best of our knowledge, this is the first time we solve an accident detection as an optimization problem and filter the frames to be selected, through a single formulation. With the camera in a surrounding infrastructure and capturing a video, we exploited the properties of sub modularity to provide a relevant and condensed key frame summary. We have studied it for various real world traffic surveillance videos comprising of vehicular accidents and thus making it a promising approach.