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This paper presents a new event-based method for detecting and trackingfeatures from the output of an event-based camera. Unlike many trackingalgorithms from the computer vision community, this process does not aim forparticular predefined shapes such as corners. It relies on a dual intertwinediterative continuous -- pure event-based -- estimation of the velocity vectorand a bayesian description of the generative feature contours. By projectingalong estimated speeds updated for each incoming event it is possible toidentify and determine the spatial location and generative contour of thetracked feature while iteratively updating the estimation of the velocityvector. Results on several environments are shown taking into account largevariations in terms of luminosity, speed, nature and size of the trackedfeatures. The usage of speed instead of positions allows for a much fasterfeedback allowing for very fast convergence rates.

Event-Based Features Selection and Tracking from Intertwined Estimation of Velocity and Generative Contours