Representing and recognizing visual dynamic events with support vector machines

Support vector machines (SVM) have been recently introduced as techniques for solving pattern recognition and regression estimation problems. SVM are derived within the framework of statistical learning theory and combine a solid theoretical foundation with very good performances in several applications. In this paper we describe a system able to detect, represent, and recognize visual dynamic events from an image sequence. While the events are initially detected by means of low-level visual processing, both the representation and recognition stages are performed with SVM. Therefore, the system is trained, instead of programmed, to perform the required tasks. The very good results obtained on real image sequences indicate that SVM can be profitably used for the construction of flexible and effective systems based on computer vision.