A Framework of Spatio-Temporal Analysis for Video Surveillance

Abstract

This paper presents a video surveillance system that is capable of detecting and classifying moving targets in real-time. The system extracts moving targets from a video stream and classifies them into predefined categories according to their spatiotemporal properties. Classification of the moving targets is completed via a combination of a temporal boosted classifier and spatiotemporal “motion energy” analysis. We illustrate that a temporal boosted classifier can be designed that successfully recognizes five object categories: person(s), bicycle, motorcycle, vehicle, and person with umbrella. The proposed temporal boosted classifier has the unique ability to improve weak classifiers by allowing them to make use of previous information when evaluating the current frame. In addition, we demonstrate a method to further process targets in the “person(s)” category to determine if they are single moving individuals or crowds. It is shown that this challenging task of moving crowd recognition can be effectively performed using spatiotemporal motion energies. These motion energies provide a rich description of a target’s dynamic characteristics, from which classification can be performed. Our empirical evaluations demonstrate that the proposed system is extremely effective at recognizing all predefined object classes.