Ms. Hayley S. Hung Profile

Hayley S. Hung

PhD Student at Queen Mary Univ of London

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Publications (1)

Proceedings Article | 28 September 2006 Paper

Detection of human interaction from a distance using salient body behaviour modelling

Hayley Hung, Shaogang Gong

Proceedings Volume 6402, 640203 (2006) https://doi.org/10.1117/12.689355

KEYWORDS: Surveillance, Feature extraction, Modeling, Motion models, Cameras, Detection and tracking algorithms, Photography, 3D modeling, Data modeling, Computer science

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Understanding far and close proximity human-human interaction observed from a distance is a necessary step towards automated suspicious or antisocial behaviour detection. Most previous work on human-human interaction has made the implicit assumption that interactions occur only at immediate spatial and temporal proximity between the subjects concerned. We propose a more realistic application of human-human interaction detection from surveillance data where the subjects of interest tend to be represented by few pixels relative to the rest of the scene. The subjects are represented by relatively few pixels since surveillance cameras are usually placed to maximise area coverage therefore there is a significant distance between the camera and the physical scene. This in itself is not so much of a disadvantage when we consider that interactions among subjects can occur between quite large distances in space. Our technique uses a spatial and temporal saliency measure to extract and select features using modifications to Kadir and Brady's scale saliency and Hung and Gong's temporal saliency algorithms respectively. From this, a hierarchical multi-scale model of a single person, his/her body pose and groups of people is formed. A person is represented by an elliptic blob where prominent oval-shaped parts are formed into a configuration. Interactions are identified by finding temporally correlated salient changes (we call events) in the probability distributions of our multi-scale configuration model. In this paper we will show how pose or configuration based models of the human body can provide a rich framework for modelling human-human interactive body behaviour even when body parts are occluded. In particular, the framework is suitable for extracting salient features from the human body where each part is represented by a few pixels in each image frame. The work is highly relevant to the development of automated systems for suspicious and antisocial behaviour detection and prevention.

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