Spatiotemporal multiple persons tracking using Dynamic Vision Sensor

Although motion analysis has been extensively investigated in the literature and a wide variety of tracking algorithms have been proposed, the problem of tracking objects using the Dynamic Vision Sensor requires a slightly different approach. Dynamic Vision Sensors are biologically inspired vision s...

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Bibliographic Details
Published in2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops pp. 35 - 40
Main Authors Piatkowska, E., Belbachir, A. N., Schraml, S., Gelautz, M.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2012
Subjects
Clustering algorithms
Data models
Dynamics
Heuristic algorithms
Machine vision
Tracking
Voltage control
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Summary:Although motion analysis has been extensively investigated in the literature and a wide variety of tracking algorithms have been proposed, the problem of tracking objects using the Dynamic Vision Sensor requires a slightly different approach. Dynamic Vision Sensors are biologically inspired vision systems that asynchronously generate events upon relative light intensity changes. Unlike conventional vision systems, the output of such sensor is not an image (frame) but an address events stream. Therefore, most of the conventional tracking algorithms are not appropriate for the DVS data processing. In this paper, we introduce algorithm for spatiotemporal tracking that is suitable for Dynamic Vision Sensor. In particular, we address the problem of multiple persons tracking in the occurrence of high occlusions. We investigate the possibility to apply Gaussian Mixture Models for detection, description and tracking objects. Preliminary results prove that our approach can successfully track people even when their trajectories are intersecting.
ISBN:1467316113
9781467316118
ISSN:2160-7508
2160-7516
DOI:10.1109/CVPRW.2012.6238892