  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.
