Event cameras, which are asynchronous bio-inspired vision sensors, have shown
great potential in computer vision and artificial intelligence. However, the
application of event cameras to object-level motion estimation or tracking is
still in its infancy. The main idea behind this work is to propose a novel deep
neural network to learn and regress a parametric object-level motion/transform
model for event-based object tracking. To achieve this goal, we propose a
synchronous Time-Surface with Linear Time Decay (TSLTD) representation, which
effectively encodes the spatio-temporal information of asynchronous retinal
events into TSLTD frames with clear motion patterns. We feed the sequence of
TSLTD frames to a novel Retinal Motion Regression Network (RMRNet) to perform
an end-to-end 5-DoF object motion regression. Our method is compared with
state-of-the-art object tracking methods, that are based on conventional
cameras or event cameras. The experimental results show the superiority of our
method in handling various challenging environments such as fast motion and low
illumination conditions.

本文提出了一种基于事件相机和深度神经网络的实时物体追踪方法，使用同步时间表面和线性时间衰减表示异步视网膜事件的信息，并通过独创的神经网络模型处理各种复杂的情况，如快速动态和低照度条件等使得实验结果表明该方法优于以往传统的物体追踪方法。