AUTHOR=Li Hongmin , Shi Luping 

TITLE=Robust Event-Based Object Tracking Combining Correlation Filter and CNN Representation

JOURNAL=Frontiers in Neurorobotics

VOLUME=Volume 13 - 2019

YEAR=2019

URL=https://www.frontiersin.org/journals/neurorobotics/articles/10.3389/fnbot.2019.00082

DOI=10.3389/fnbot.2019.00082

ISSN=1662-5218

ABSTRACT=Object tracking based on an 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 object tracking method based on correlative filter mechanism and convolutional neural network (CNN) representation. 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 pre-trained CNN are used to represent the appearance of the rate encoded event-stream object. Results prove that the proposed 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.