用事件相机提升任意点跟踪精度，解决运动模糊与稀疏事件问题
MATE: Motion-Augmented Temporal Consistency for Event-based Point Tracking
- 引入运动引导模块，结合运动矢量优化局部匹配
- 在仿真数据集上使存活率提升17.9%，优于纯事件基线
- 适用于高速或非线性运动场景，适合事件相机应用者
任意点跟踪（TAP）在运动分析中至关重要。视频方法依赖帧间迭代局部匹配，但假设盲区间为线性运动，导致大位移或非线性运动下点丢失。事件相机具有高时间分辨率和无运动模糊特性，能以微秒级精度捕捉连续运动信息。本文提出一种基于事件的任意点跟踪框架，通过两个定制模块应对事件空间稀疏性和运动敏感性挑战。为解决事件稀疏带来的歧义，设计运动引导模块，将运动矢量融入局部匹配过程；同时引入可变运动感知模块，确保对不同速度的响应保持时序一致性，从而提升匹配精度。为验证方法有效性，通过仿真构建两个用于任意点跟踪的事件数据集。实验表明，该方法在 $Survival_{50}$ 指标上比纯事件基线提升17.9%。此外，在标准特征跟踪基准上，性能超越所有现有方法，包括融合事件与视频帧的方法。
原文摘要 · Abstract (English)
Tracking Any Point (TAP) plays a crucial role in motion analysis. Video-based approaches rely on iterative local matching for tracking, but they assume linear motion during the blind time between frames, which leads to point loss under large displacements or nonlinear motion. The high temporal resolution and motion blur-free characteristics of event cameras provide continuous, fine-grained motion information, capturing subtle variations with microsecond precision. This paper presents an event-based framework for tracking any point, which tackles the challenges posed by spatial sparsity and motion sensitivity in events through two tailored modules. Specifically, to resolve ambiguities caused by event sparsity, a motion-guidance module incorporates kinematic vectors into the local matching process. Additionally, a variable motion aware module is integrated to ensure temporally consistent responses that are insensitive to varying velocities, thereby enhancing matching precision. To validate the effectiveness of the approach, two event dataset for tracking any point is constructed by simulation. The method improves the $Survival_{50}$ metric by 17.9% over event-only tracking of any point baseline. Moreover, on standard feature tracking benchmarks, it outperforms all existing methods, even those that combine events and video frames.
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