A Petri Net-based Approach for Event Detection in Pedestrian Crossing Sequence

In this paper we present an approach to automatically detect anomalous traffic events like pedestrians crossing the junction, based on traffic video of low-level features such as size of the blob, spatial location, and velocity. The construction of Petri-Nets was used for both semantic description and event detection within traffic videos. The major novelties of this paper are extensions to both the modeling and the recognition capacities of Object Petri-Nets (PN). The detection of object level features are done with the help of state of art techniques like Gaussian Mixture of Models (GMM), and a series of Petri-Nets composed of various objects is proposed to describe the video content. The expected outcome of the proposed framework is that we can easily build semantic detectors based on PNs to search within traffic videos and identify interesting events. Experimental results based on recorded traffic video data sets and synthetic data sets are used to illustrate the potential of this framework.