Robust event-stream pattern tracking based on correlative filter
Hongmin Li, and Luping Shi

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…
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Taxonomy
TopicsAdvanced Memory and Neural Computing · CCD and CMOS Imaging Sensors · EEG and Brain-Computer Interfaces
