用强化学习动态调整事件累积，让摄像头跟踪更准更快。
GoStop: Reinforcement Learning for Adaptive Temporal Aggregation in Event-Based Feature Tracking
- 用强化学习决定何时积累事件或执行追踪
- 在动态运动下比传统方法提升追踪准确率和效率
- 适合需要实时、抗干扰视觉追踪的机器人场景
特征追踪在理解场景运动中起基础作用，支持多种下游任务。事件相机具有高时间分辨率和异步感知能力，能实现低延迟、抗运动干扰的感知，非常适合在快速非线性运动下进行特征追踪。然而，现有事件基特征追踪方法依赖于手工调参的固定启发式规则进行事件累积，无法适应多变的运动动态，导致在突发运动变化或低速运动场景下性能下降。本文将事件累积建模为序列决策问题，引入强化学习（RL）框架，自适应控制在线事件基特征追踪中的累积过程。我们的方法训练一个RL代理，根据运动线索判断是否继续累积事件或执行追踪推理。所提出的自适应时间代理可在无需人工规则的前提下，动态适应不同运动模式。此外，我们构建了动态事件追踪（DEFT）数据集，包含动态运动分布，用于评估追踪鲁棒性。大量实验表明，将该即插即用框架集成到现有追踪方法中，均显著优于基于启发式的方法，在动态运动下提升鲁棒性，同时更好平衡追踪精度与效率。项目代码与数据集已开源。
原文摘要 · Abstract (English)
Feature tracking plays a fundamental role in understanding scene motion and supports various downstream tasks. Event cameras, with their high temporal resolution and asynchronous sensing, enable low-latency and motion-robust perception, making them well-suited for feature tracking under fast and non-linear motion. However, existing event-based feature tracking methods rely on fixed heuristic rules based on hand-tuning for event accumulation. Such strategies fail to adapt to diverse motion dynamics, leading to degraded performance under abrupt motion changes or low-motion scenarios. In this paper, we model event accumulation as a sequential decision-making problem and introduce reinforcement learning (RL) framework to adaptively control the accumulation process for online event-based feature tracking. Our approach trains a RL agent that decides whether to continue accumulating events or to perform tracking inference based on motion cues. The proposed adaptive temporal agent enables dynamic adaptation to varying motion patterns without relying on hand-crafted rules. Furthermore, we introduce a Dynamic Event-based Tracking (DEFT) dataset with dynamic motion distributions to evaluate the robustness of the feature tracking. Extensive experiments demonstrate that integrating our plug-and-play framework to existing feature tracking methods consistently outperforms heuristic-based approaches, improving robustness under dynamic motion while offering a better balance between tracking accuracy and efficiency. Our project codes and datasets are available at https://github.com/kmax2001/GoSTOP
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