A Surveillance System for the Recognition of Intent within Individuals and Crowds

Cohen, Charles J.; Morelli, Frank; Scott, K. Andrea

doi:10.1109/ths.2008.4534514

Cited by 23 publications

(17 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The idea is to partition the observed human movements into some discrete states and then classify them appropriately. Apparently, partitioning of the observed movements is very application-specific and overall hard to predict what will constitute suspicious or endangering behavior (Cohen et al, 2008;Jan, 2004;Saligrama et al, 2010). Most approaches in the field of video understanding incorporated methods for detection of domain-specific events (Bremond et al, 2006).…”

Section: Behaviour Analysis and Understandingmentioning

confidence: 99%

“…In his seminal work, Johansson (1973) revealed that presenting coordinated human joint motion was sufficient for rendering the impression of a human being walking or running through space. With respect to detecting hostile intent (Cohen et al, 2008), each point in the point-light walker (PLW) might have its own gesture motion -which when examined in relation to the links in the object, can be used to determine the overall state of the system. Unusual events such as vandalism or overcrowded areas can be detected by unusual movements as well as unlikely object positions.…”

Section: Wwwintechopencommentioning

confidence: 99%

“…A system will be able to learn the visual cues found to be of some significance in identifying an emotion (Johansson, 1973) by identifying specific regions of the body that identify emotions,. Researchers will discover that motions of certain body parts may identify an emotion more than others (Cohen et al, 2008;Johansson, 1973;Montepare et al, 1987). For instance, researchers may discover that in anger the torso is most evocative of that emotion.…”

Section: Wwwintechopencommentioning

confidence: 99%

“…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). The main intent of this paper is to give engineers, scientists, and/or managers alike, a high-level, general understanding of both the theoretical and practical perspectives involved with a visual surveillance system and its potential challenges while considering implementing or integrating a visual surveillance system.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Survey on Behavior Analysis in Video Surveillance Applications

Ko¹

2011

Video Surveillance

View full text Add to dashboard Cite

Section: Behaviour Analysis and Understandingmentioning

confidence: 99%

Section: Wwwintechopencommentioning

confidence: 99%

Section: Wwwintechopencommentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Survey on Behavior Analysis in Video Surveillance Applications

Ko¹

2011

Video Surveillance

View full text Add to dashboard Cite

“…Detection of suspicious human behavior involves modeling and classification of human activities with certain rules [5]. Modeling and classification of human activities are not trivial due to the randomness and complex nature of human movement.…”

Section: Behavior Analysis and Understandingmentioning

confidence: 99%

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

2008

2008 37th IEEE Applied Imagery Pattern Recognition Workshop

174

View full text Add to dashboard Cite

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.

show abstract