Event-ECC: Asynchronous Tracking of Events with Continuous Optimization
This work addresses incremental improvements in event-based vision for applications like robotics and real-time tracking.
The paper tackles the problem of tracking features in event-based vision by proposing a direct matching scheme that aligns spatial distributions of events over time, resulting in improved tracking accuracy and feature age compared to state-of-the-art asynchronous trackers.
In this paper, an event-based tracker is presented. Inspired by recent advances in asynchronous processing of individual events, we develop a direct matching scheme that aligns spatial distributions of events at different times. More specifically, we adopt the Enhanced Correlation Coefficient (ECC) criterion and propose a tracking algorithm that computes a 2D motion warp per single event, called event-ECC (eECC). The complete tracking of a feature along time is cast as a \emph{single} iterative continuous optimization problem, whereby every single iteration is executed per event. The computational burden of event-wise processing is alleviated through a lightweight version that benefits from incremental processing and updating scheme. We test the proposed algorithm on publicly available datasets and we report improvements in tracking accuracy and feature age over state-of-the-art event-based asynchronous trackers.