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

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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.
OriginalsprogEngelsk
TitelProceedings of the 10th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2015)
RedaktørerJosé Braz, Sebastiano Battiato, Francisco Imai
Vol/bind2
ForlagSciTePress
Publikationsdato2015
Sider244-251
ISBN (Trykt)978-989-758-090-1
DOI
StatusUdgivet - 2015
BegivenhedInternational Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications 2015 - Berlin, Tyskland
Varighed: 11 mar. 201514 mar. 2015
Konferencens nummer: 11

Konference

KonferenceInternational Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications 2015
Nummer11
Land/OmrådeTyskland
ByBerlin
Periode11/03/201514/03/2015

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