Comparison of Multi-shot Models for Short-term Re-identification of People using RGB-D Sensors

Publication: Research - peer-reviewArticle in proceeding

Abstract

This work explores different types of multi-shot descriptors for re-identification in an on-the-fly enrolled environment using RGB-D sensors. We present a full re-identification pipeline complete with detection, segmentation, feature extraction, and re-identification, which expands on previous work by using multi-shot descriptors modeling people over a full camera pass instead of single frames with no temporal linking. We compare two different multi-shot models; mean histogram and histogram series, and test them each in 3 different color spaces. Both histogram descriptors are assisted by a depth-based pruning step where unlikely candidates are filtered away. Tests are run on 3 sequences captured in different circumstances and lighting situations to ensure proper generalization and lighting/environment invariance.
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This work explores different types of multi-shot descriptors for re-identification in an on-the-fly enrolled environment using RGB-D sensors. We present a full re-identification pipeline complete with detection, segmentation, feature extraction, and re-identification, which expands on previous work by using multi-shot descriptors modeling people over a full camera pass instead of single frames with no temporal linking. We compare two different multi-shot models; mean histogram and histogram series, and test them each in 3 different color spaces. Both histogram descriptors are assisted by a depth-based pruning step where unlikely candidates are filtered away. Tests are run on 3 sequences captured in different circumstances and lighting situations to ensure proper generalization and lighting/environment invariance.
Original languageEnglish
Title of host publicationProceedings of the 10th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2015)
EditorsJosé Braz, Sebastiano Battiato, Francisco Imai
Volume2
PublisherSCITEPRESS Digital Library
Publication date2015
Pages244-251
ISBN (print)978-989-758-090-1
DOI
StatePublished - 2015
Event - Berlin, Germany

Conference

ConferenceInternational Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications 2015
Nummer11
LandGermany
ByBerlin
Periode11/03/201514/03/2015

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