A survey on behavior analysis in video surveillance for homeland security applications

Abstract

Surveillance cameras are inexpensive and everywhere these days but the manpower required to monitor and analyze them is expensive. Consequently the videos from these cameras are usually monitored sparingly or not at all; they are often used merely as archive, to refer back to once an incident is known to have taken place. Surveillance cameras can be a far more useful tool if instead of passively recording footage, they can be used to detect events requiring attention as they happen, and take action in real time. This is the goal of automated visual surveillance: to obtain a description of what is happening in a monitored area, and then to take appropriate action based on that interpretation. Video surveillance for humans is one of the most active research topics in computer vision. It has a wide spectrum of promising homeland security applications. Video management and interpretation systems have become quite capable in recent years. This paper looks into how hardware and software can be put together to solve surveillance problems in an age of increased concern with public safety and security. In general, the framework of a video surveillance system includes the following stages: modeling of environments, detection of motion, classification of moving objects, tracking, behavior understanding and description, and fusion of information from multiple cameras. 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. This paper reviews developments and general strategies of stages involved in video surveillance, and analyzes the feasibility and challenges for combining motion analysis, behavior analysis, and standoff biometrics for identification of known suspects, anomaly detection, and behavior understanding.

Fuzzy human motion analysis: A review

“…[7,8,9,20,21,114,115] Fuzzy Kalman filters are capable of solving the divergence problem by incorporating the FIS, and are more robust against the streams of random noisy data inputs.…”

“…Here, the HMA concerns the detection, tracking and recognition of human, and more generally the understanding of human behaviors, from image sequences involving humans. Amongst all, video surveillance is one of the most important real-time applications [6,7,8,9,10]. For instance, as illustrated in Figure 1, the Madrid, London and Boston marathon bombing tragedies, happened in 2004, 2005 and 2013 respectively, would not have been worse if an intelligent video surveillance system that capable of automatically detecting abnormal human behavior was installed in the public areas.…”

Qualitative and Quantitative Spatio-temporal Relations in Daily Living Activity Recognition

“…Despite its long research history [1][2][3][4] finding a universal semantic representation for activity analysis is still a difficult challenge due to the complexity of human activities and the variability of how these activities can be performed, even by the same person. Activity analysis is often investigated from a security domain perspective, as automatic recognition of human behaviour in sensitive areas is a critical issue for video surveillance [5][6][7]. Recently however, understanding daily human activities has also become popular in moving towards smart environments and robotic assistive living, where activity analysis is a vital component for their effective operation.…”

A Survey on Behavior Analysis in Video Surveillance Applications

“…However, due to the difficulty of developing general surveillance algorithms, a visual surveillance system usually has had to be designed as a collection of separate algorithms, which are selected on a case by case basis. The flow and organization of this review paper has followed four very thorough, excellent surveys conducted by (Ko, 2008;Wang et al, 2003;Hu et al, 2004;Kumar et al, 2008) when discussing the general framework of automated visual surveillance systems as shown in Fig. 1, enriching with the general architecture of a video understanding system (Bremond et al, 2006) in behavior analysis and with expandable network system architecture as illustrated in (Cohen et al, 2008).…”