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Human Posture Recognition for Behaviour Understanding

by Bernard Boulay



During this thesis, we have proposed a real-time, generic, and operational approach to recognising human posture with one static camera. The approach is fully automatic and independent from the viewpoint of the camera. Human posture recognition from a video sequence is a difficult task. This task is part of the more general problem of video sequence interpretation. The proposed approach takes as input information provided by vision algorithms such as the silhouette of the observed person (a binary image representing the person and the background), or her/his position in the scene.

The first contribution is the modelling of a 3D posture avatar. This avatar is composed of a human model (defining the relations between the different body parts), a set of parameters (defining the position of the body parts) and a set of body primitives (defining the visual aspect of the body parts).

The second contribution is the proposed hybrid approach to recognise human posture. This approach combines the use of 3D posture avatar and 2D techniques. The 3D avatars are used in the recognition process to acquire a certain independence from the camera viewpoint. The 2D techniques represent the silhouettes of the observed person to provide real-time processing. The proposed approach is composed of two main parts: the posture detection which recognises the posture of the detected person by using information computed on the studied frame, and the posture temporal filtering which filters the posture by using information about the posture of the person on the previous frames.

A third contribution is the comparison of different 2D silhouette representations. The comparison is made in terms of computation time and dependence on the silhouette quality. Four representations have been chosen: geometric features, Hu moments, skeletonisation, and the horizontal and vertical projections.

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