IEEE Trans Neural Netw Learn Syst - Asynchronous Event-Based Multikernel Algorithm for High-Speed Visual Features Tracking.

Tópicos

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Resumo

This paper presents a number of new methods for visual tracking using the output of an event-based asynchronous neuromorphic dynamic vision sensor. It allows the tracking of multiple visual features in real time, achieving an update rate of several hundred kilohertz on a standard desktop PC. The approach has been specially adapted to take advantage of the event-driven properties of these sensors by combining both spatial and temporal correlations of events in an asynchronous iterative framework. Various kernels, such as Gaussian, Gabor, combinations of Gabor functions, and arbitrary user-defined kernels, are used to track features from incoming events. The trackers described in this paper are capable of handling variations in position, scale, and orientation through the use of multiple pools of trackers. This approach avoids the N(2) operations per event associated with conventional kernel-based convolution operations with N ? N kernels. The tracking performance was evaluated experimentally for each type of kernel in order to demonstrate the robustness of the proposed solution.

Resumo Limpo

paper present number new method visual track use output eventbas asynchron neuromorph dynam vision sensor allow track multipl visual featur real time achiev updat rate sever hundr kilohertz standard desktop pc approach special adapt take advantag eventdriven properti sensor combin spatial tempor correl event asynchron iter framework various kernel gaussian gabor combin gabor function arbitrari userdefin kernel use track featur incom event tracker describ paper capabl handl variat posit scale orient use multipl pool tracker approach avoid n oper per event associ convent kernelbas convolut oper n n kernel track perform evalu experiment type kernel order demonstr robust propos solut

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