基于事件的异步跟踪算法，用连续优化提升精度与稳定性。
Event-ECC: Asynchronous Tracking of Events with Continuous Optimization
- 以单个事件为单位，通过2D运动扭曲实现连续优化跟踪
- 在公开数据集上追踪精度和特征年龄均优于现有方法
- 轻量级增量更新设计，适合实时事件驱动系统
本文提出一种基于事件的追踪算法。受近期异步处理单个事件进展启发，我们设计了一种直接匹配方案，用于对不同时间点的事件空间分布进行对齐。具体而言，采用增强相关系数（ECC）准则，提出一种每单个事件计算二维运动扭曲的追踪算法，称为事件-ECC（eECC）。整个特征的时间追踪被建模为一个单一的迭代连续优化问题，每次迭代仅针对一个事件执行。通过轻量级版本结合增量处理与更新机制，显著降低了事件级处理的计算负担。我们在公开数据集上测试该算法，结果表明其在追踪精度和特征年龄方面均优于当前最先进的事件基异步追踪器。
原文摘要 · Abstract (English)
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.
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