With the spread of wearable cameras, many consumer applications ranging from social tagging to video summarization would greatly benefit from people re-identification methods capable of dealing with the egocentric perspective. In this regard, first-person camera views present such a unique setting that traditional re-identification methods results in poor performance when applied to this scenario. In this paper, we present a simple but effective solution that overcomes the limitations of traditional approaches by dividing people images into meaningful body parts. Furthermore, by taking into account human gaze information concerning where people look at when trying to recognize a person, we devise a meaningful way to weight the contributions of different bodyparts. Experimental results validate the proposal on a novel egocentric re-identification dataset, the first of its kind, showing that the performance increases when compared to current state of the art on egocentric sequences is significant.

Body Part Based Re-identification from an Egocentric Perspective / Fergnani, Federica; Alletto, Stefano; Serra, Giuseppe; De Mira, Joaquim; Cucchiara, Rita. - (2016). (Intervento presentato al convegno Computer Vision and Pattern Recognition tenutosi a Las Vegas, USA nel 26/06/2016) [10.1109/CVPRW.2016.51].

Body Part Based Re-identification from an Egocentric Perspective

ALLETTO, STEFANO;SERRA, GIUSEPPE;CUCCHIARA, Rita
2016

Abstract

With the spread of wearable cameras, many consumer applications ranging from social tagging to video summarization would greatly benefit from people re-identification methods capable of dealing with the egocentric perspective. In this regard, first-person camera views present such a unique setting that traditional re-identification methods results in poor performance when applied to this scenario. In this paper, we present a simple but effective solution that overcomes the limitations of traditional approaches by dividing people images into meaningful body parts. Furthermore, by taking into account human gaze information concerning where people look at when trying to recognize a person, we devise a meaningful way to weight the contributions of different bodyparts. Experimental results validate the proposal on a novel egocentric re-identification dataset, the first of its kind, showing that the performance increases when compared to current state of the art on egocentric sequences is significant.
2016
Computer Vision and Pattern Recognition
Las Vegas, USA
26/06/2016
Fergnani, Federica; Alletto, Stefano; Serra, Giuseppe; De Mira, Joaquim; Cucchiara, Rita
Body Part Based Re-identification from an Egocentric Perspective / Fergnani, Federica; Alletto, Stefano; Serra, Giuseppe; De Mira, Joaquim; Cucchiara, Rita. - (2016). (Intervento presentato al convegno Computer Vision and Pattern Recognition tenutosi a Las Vegas, USA nel 26/06/2016) [10.1109/CVPRW.2016.51].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1104565
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