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VIDEO - BASED HUMAN BEHAVIOUR INTERPRETATION AND MODELLING WITH THE STATBOX CONCEPT
Abstract
Detecting people in videos or images in order to interpret their behaviour is nowadays an important part of safety and security monitoring. Detection of persons in videos is more challenging than detection of any other moving objects, be it animals or vehicles. This paper reviews the recent work on a developing tool - STATBOX for spatial analysis and modelling. STATBOX concept is based on the video data collection for a given territory, which can be taken using different devices such as mobile phones, surveillance video cameras, as well as statistical data obtained from video files. Each video must contain geolocalization data and time information as well as other markers, which make it possible to determine the precise direction of information flow in the video. All information about moving objects in the video is extracted from each video frame. The extracted information on all determined objects contains their contours, proportions, textures, and markers, as well as the geolocalization information пїЅ spatial data and time data. Further on, this information is compared with information collected by other devices using a classification engine, and this allows for reconstruction of the object movements in the given territory. Using STATBOX minimizes spatial analysis costs in the urban environment, and it has a flexibility of creation automated adequate geosimulation models - agent based modelling on GAML patterns and GIS. So far the STATBOX concept was successfully used in pedestrian counting with recognition in the city park for flows simulation and analysis, as well as in campus for student behaviour analysis and movement flow simulation. This shows that the STATBOX concept can be used for better understanding of human behaviour based on video data, and its enhanced automation can bring great benefits in the areas such as public security surveillance, traffic control and pedestrian flow analysis, crisis management and similar.
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