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

TL;DR

This paper introduces a novel event-based feature detection and tracking method that jointly estimates velocity and contours without predefined shape assumptions, enabling fast and robust tracking in diverse environments.

Contribution

It proposes a dual iterative approach combining velocity estimation and contour modeling from event data, improving tracking speed and robustness over traditional shape-specific methods.

Findings

Effective in environments with varying luminosity and feature sizes

Achieves fast convergence due to velocity-based feedback

Handles diverse feature types without predefined shape constraints

Abstract

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.