Reconnaissance des actions humaines à partir d'une séquence vidéo
The work done in this master's thesis, presents a new system for the  recognition of human actions from a video sequence.  The system uses,  as input, a video sequence taken by a static camera.  A binary  segmentation method of the the video sequence is first achieved, by a  learning algorithm, in order to detect and extract the different people  from the background.  To recognize an action, the system then exploits  a set of prototypes generated from an MDS-based dimensionality  reduction technique, from two different points of view in the video  sequence.  This dimensionality reduction technique, according to two  different viewpoints, allows us to model each human action of the  training base with a set of prototypes (supposed to be similar for  each class) represented in a low dimensional non-linear space.  The  prototypes, extracted according to the two viewpoints, are fed to a  $K$-NN classifier which allows us to identify the human action that  takes place in the video sequence.  The experiments of our model  conducted on the Weizmann dataset of human actions provide interesting  results compared to the other state-of-the art (and often more  complicated) methods.  These experiments show first the  sensitivity of our model for each viewpoint and its effectiveness to  recognize the different actions, with a variable but satisfactory  recognition rate and also the results obtained by the fusion of these  two points of view, which allows us to achieve a high performance   recognition rate.
Video processing Human gait analysis Gesture recognition Reduction of dimensionality Shape recognition