Irene Perez-Salesa

Irene Perez-Salesa, Rodrigo Aldana-Lopez, Carlos Sagues

Visual object tracking under challenging conditions of motion and light can be hindered by the capabilities of conventional cameras, prone to producing images with motion blur. Event cameras are novel sensors suited to robustly perform vision tasks under these conditions. However, due to the nature of their output, applying them to object detection and tracking is non-trivial. In this work, we propose a framework to take advantage of both event cameras and off-the-shelf deep learning for object tracking. We show that reconstructing event data into intensity frames improves the tracking performance in conditions under which conventional cameras fail to provide acceptable results.

Ariana Méndez-Castillo, Irene Perez-Salesa, Rodrigo Aldana-López et al.

This paper studies event-triggered parameter estimation in sensor fusion systems where sensors transmit measurements to a gradient based estimator. We introduce a regressor-driven local triggering rule that requires no knowledge of the current parameter estimate and depends solely on the regressor signals. Under a persistent excitation condition on the aggregate regressor, we derive explicit design inequalities on the estimator gain and event thresholds that guarantee global exponential convergence. The analysis is based on a time-varying Lyapunov function. We further provide a sufficient condition on the regressor dynamics that enforces a uniform lower bound on inter-event times, excluding Zeno behavior. Simulations show substantial communication savings while preserving exponential convergence.