Robust event-stream pattern tracking based on correlative filter

TL;DR

This paper introduces a robust, high-speed event-stream pattern tracking method using correlative filters and deep CNN features, effectively handling noise, occlusion, and complex backgrounds in dynamic vision sensor data.

Contribution

It proposes a novel correlative filter-based tracking approach utilizing deep CNN features for event-stream data, improving robustness and speed in complex scenes.

Findings

Effective in noisy and occluded environments

Robust to scale, pose, and deformation variations

Achieves high-speed tracking performance

Abstract

Object tracking based on retina-inspired and event-based dynamic vision sensor (DVS) is challenging for the noise events, rapid change of event-stream shape, chaos of complex background textures, and occlusion. To address these challenges, this paper presents a robust event-stream pattern tracking method based on correlative filter mechanism. In the proposed method, rate coding is used to encode the event-stream object in each segment. Feature representations from hierarchical convolutional layers of a deep convolutional neural network (CNN) are used to represent the appearance of the rate encoded event-stream object. The results prove that our method not only achieves good tracking performance in many complicated scenes with noise events, complex background textures, occlusion, and intersected trajectories, but also is robust to variable scale, variable pose, and non-rigid deformations. In addition, this correlative filter based event-stream tracking has the advantage of high speed. The proposed approach will promote the potential applications of these event-based vision sensors in self-driving, robots and many other high-speed scenes.