Abstract
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.
Highlights
IntroductionIn Computer Vision Community, the process of monitoring the abnormal behaviour of people and objects automatically, within public places, especially airports, metro stations and traffic intersections, by means of visual aids (cameras), has increased substantially, for safety and security purposes
In Computer Vision Community, the process of monitoring the abnormal behaviour of people and objects automatically, within public places, especially airports, metro stations and traffic intersections, by means of visual aids, has increased substantially, for safety and security purposes
Most of the Events are complex in surveillance videos are difficult to model because of the complex logical and temporal relations that exists between different objects
Summary
In Computer Vision Community, the process of monitoring the abnormal behaviour of people and objects automatically, within public places, especially airports, metro stations and traffic intersections, by means of visual aids (cameras), has increased substantially, for safety and security purposes. Automating the process of video event detection and recognition is one important task of computer vision research In most of these systems, the goal is to reduce the gap between high level semantics representing human’s activity and lower level data given by vision modules. Our approach consists of Lower level vision modules to extract primitive events These primitive events are processed by Petri nets filters to detect composite events of interest. At any time during the interpretation process, the positions of tokens in the Petri net summarize what happened in the past (keep history) and predict what will happen in the future In this way, composite events are recognized incrementally and there is no need to re-evaluate past events.
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