  This paper presents a new event-based method for detecting and tracking
features from the output of an event-based camera. Unlike many tracking
algorithms from the computer vision community, this process does not aim for
particular predefined shapes such as corners. It relies on a dual intertwined
iterative continuous -- pure event-based -- estimation of the velocity vector
and a bayesian description of the generative feature contours. By projecting
along estimated speeds updated for each incoming event it is possible to
identify and determine the spatial location and generative contour of the
tracked feature while iteratively updating the estimation of the velocity
vector. Results on several environments are shown taking into account large
variations in terms of luminosity, speed, nature and size of the tracked
features. The usage of speed instead of positions allows for a much faster
feedback allowing for very fast convergence rates.
