Event-ECC: Asynchronous Tracking of Events with Continuous Optimization

TL;DR

This paper introduces event-ECC, an asynchronous event-based tracking algorithm that uses continuous optimization with ECC to improve accuracy and efficiency in tracking features over time.

Contribution

The paper presents a novel event-based tracking method employing continuous optimization and ECC, enabling per-event warp computation and improved tracking performance.

Findings

Enhanced tracking accuracy over state-of-the-art methods

Efficient incremental processing reduces computational load

Effective feature age and robustness improvements

Abstract

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.