A Review of Human Activity Recongnition and Behavior Understanding in Video Surveillance

Abstract

This paper presents a survey of activity recognition and understanding the behaviour of human activity in video sequence. The major goal of this paper is to provide a general review on the overall process of a surveillance system used in the current trend. Visual surveillance system is directed on automatic identification of events of interest, especially on tracking and classification of moving objects. The processing step of the video surveillance system includes the following stages: Surrounding model, object representation, object tracking, activity recognition and behaviour understanding. It describes techniques that use to define a general set of activities that are applicable to a wide range of scenes and environments in video sequence. The review methods used for real-time surveillance through a set of events for further analysis triggering, including virtual fencing, speed profiling, behaviour classification, anomaly detection, and object interaction.

Human Detection Using Gradient Maps and Golden Ratio

“…During the scaling procedure, both maps are divided evenly into square blocks with length equal to S. For the magnitude maps, the local maximum magnitude values inside each block are saved. While for the orientation maps, both positive and negative directions of the gradient along x and y axes are used to obtain the orientation for the scaled map, as given in Equation (5). (5) where θ S is the new orientation value for the scaled orientation map with the scaling factor S, and are the maximum positive and negative x components of the gradient respectively, and are the maximum positive and negative y components of gradient respectively.…”

“…The basic functions of human detection are used to detect and track the human, while the advanced functions which contain some form of post-processing are used to achieve additional goals. These goals may include people counting [3], face recognition [4] and behaviour recognition [5]. The advanced functions are generally used for high security environments such as airports and casinos, these systems tend to be expensive which equipped with high speed communication lines and powerful processors [6].…”