Sensors fusion for head tracking using Particle filter in a context of falls detection
In the context of ageing societies, assessing risk factors and detecting falls for the elderly is becoming a crucial issue. In this paper, we propose an iterative head tracking method based on particle filtering using the fusion of low cost thermal and depth sensors for home environments whilst preserving privacy. The iteration process begins by segmenting the head in the depth image to calculate the depth coefficients and the thermal coefficients used for updating the particle weights. The method was tested on several sequences, with or without depth-thermal fusion: results show its robustness and accuracy , and also demonstrate that fusion improves tracking, namely when fast motion occurs (in case of a fall for instance) or when segmentation is erroneous.
We study the effects of gluon radiation on top production and decay processes at an $e^+e^-$ collider.The matrix elements are computed without any approximations, using spinor techniques. We use a Monte Carlo event generator which takes into account the infrared singularity due to soft gluons and differences in kinematics associated with radiation in the production versus decay process. The calculation is illustrated for several strategies of top mass reconstruction.