Video surveillance is recently one of the most active research
topics in computer vision. It has a wide spectrum of promising
public safety and security applications. As the number of
cameras exceeds the capability of human operators to monitor
them, the traditional passive video surveillance is proving
ineffective. Hence, converting to intelligent visual
surveillance is inevitable. Intelligent visual surveillance aims
to detect, recognize and track certain objects from image
sequences automatically, and more generally to understand
and describe object behaviors. Many researchers have
contributed to the field of automated video surveillance
through detection, classification, and tracking algorithms.
Despite recent progress in computer vision and other related
areas, there are still major technical challenges to be
overcome before reliable automated video surveillance can be
realized. Recently, the problem of analyzing behavior in
videos has been the focus of several researchers’ efforts. It
aims to analyze and interpret individual behaviors and
interactions between different objects found in the scene.
Hence, obtaining a description of what is happening in a
monitored area, and then taking appropriate action based on
that interpretation. In this paper, we give a survey of behavior
analysis work in video surveillance and compare the
performance of the state-of-the-art algorithms on different
datasets. Moreover, useful datasets are analyzed in order to
provide help for initiating research projects. |
