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Machine Vision and Applications
Erschienen in: Machine Vision and Applications 1-2/2017

23.07.2016 | Original Paper

Directional coherence-based spatiotemporal descriptor for object detection in static and dynamic scenes

verfasst von: Wonjun Kim, Jae-Joon Han

Erschienen in: Machine Vision and Applications | Ausgabe 1-2/2017

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Abstract

This paper presents a simple, yet powerful local descriptor, so-called the histograms of space–time dominant orientations (HiSTDO). Specifically, our HiSTDO is composed of two main components, i.e., the dominant orientation and its coherence, which represents how intensively gradients in the local region are distributed along the space–time dominant orientation. By incorporating them into the histogram, we define it as our HiSTDO descriptor. In contrast to previous methods vulnerable to the presence of the background clutter and the camera noise, our HiSTDO greatly encodes the space–time shape of underlying structures even under such challenging conditions, and it can thus be efficiently applied to various applications (e.g., object and action detection). Experimental results on diverse datasets demonstrate that the proposed descriptor is effective for human action as well as object detection.
Vorheriger Artikel Abnormality detection in crowd videos by tracking sparse components
Nächster Artikel Toward evaluation of visual navigation algorithms on RGB-D data from the first- and second-generation Kinect

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Metadaten
Titel
Directional coherence-based spatiotemporal descriptor for object detection in static and dynamic scenes
verfasst von
Wonjun Kim
Jae-Joon Han
Publikationsdatum
23.07.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
Machine Vision and Applications / Ausgabe 1-2/2017
Print ISSN: 0932-8092
Elektronische ISSN: 1432-1769
DOI
https://doi.org/10.1007/s00138-016-0801-7

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