Event cameras produce asynchronous discrete outputs due to the independent response of camera pixels to changes in brightness. The asynchronous and discrete nature of event data facilitate the tracking of prolonged feature trajectories. Nonetheless, this necessitates the adaptation of feature tracking techniques to efficiently process this type of data. In addressing this challenge, we proposed a hybrid data-driven feature tracking method that utilizes data from both event cameras and frame-based cameras to track features asynchronously. It mainly includes patch initialization, patch optimization, and patch association modules. In the patch initialization module, FAST corners are detected in frame images, providing points responsive to local brightness changes. The patch association module introduces a nearest-neighbor (NN) algorithm to filter new feature points effectively. The patch optimization module assesses optimization quality for tracking quality monitoring. We evaluate the tracking accuracy and robustness of our method using public and self-collected datasets, focusing on average tracking error and feature age. In contrast to the event-based Kanade–Lucas–Tomasi tracker method, our method decreases the average tracking error ranging from 1.3 to 29.2% and boosts the feature age ranging from 9.6 to 32.1%, while ensuring the computational efficiency improvement of 1.2–7.6%. Thus, our proposed feature tracking method utilizes the unique characteristics of event cameras and traditional cameras to deliver a robust and efficient tracking system.

由于相机像素对亮度变化的独立响应，事件相机产生异步离散输出。事件数据的异步和离散特性有利于跟踪长期特征轨迹。尽管如此，这需要采用特征跟踪技术来有效地处理此类数据。为了应对这一挑战，我们提出了一种混合数据驱动的特征跟踪方法，该方法利用来自事件摄像机和基于帧的摄像机的数据来异步跟踪特征。主要包括补丁初始化、补丁优化、补丁关联模块。在补丁初始化模块中，检测帧图像中的 FAST 角点，提供响应局部亮度变化的点。补丁关联模块引入了最近邻（NN）算法来有效过滤新的特征点。补丁优化模块评估优化质量以进行跟踪质量监控。我们使用公共和自行收集的数据集评估我们方法的跟踪准确性和鲁棒性，重点关注平均跟踪误差和特征年龄。与基于事件的 Kanade–Lucas–Tomasi 跟踪器方法相比，我们的方法将平均跟踪误差降低了 1.3% 到 29.2%，将特征年龄提高了 9.6% 到 32.1%，同时确保计算效率提高 1.2–7.6 %。因此，我们提出的特征跟踪方法利用事件摄像机和传统摄像机的独特特性来提供稳健且高效的跟踪系统。