The interest of the research community in creating reference datasets for performance analysis is always very high. Although new datasets, collecting large amounts of video footage are spreading in surveillance and forensics, few bench-marks with annotation data are available for testing specific tasks and especially for 3D/multi-view analysis. In this paper we present 3DPeS, a new dataset for 3D/multi- view surveillance and forensic applications. This has been designed for discussing and evaluating research results in people re-identification and other related activities (people detection, people segmentation and people tracking). The new assessed version of the dataset contains hundreds of video sequences of 200 people taken from a multi-camera distributed surveillance system over several days, with different light conditions; each person is detected multiple times and from different points of view. In surveillance scenarios, the dataset can be exploited to evaluate people reacquisition, 3D body models and people activity reconstruction algorithms. In forensics it can be adopted too, by relaxing some constraints (e.g. real time) and neglecting some information (e.g. calibration). Some results on this new dataset are presented using state of the art methods for people re-identification as a benchmark for future comparisons.

3DPes: 3D People Dataset for Surveillance and Forensics / Baltieri, Davide; Vezzani, Roberto; Cucchiara, Rita. - ELETTRONICO. - 1:(2011), pp. 59-64. ((Intervento presentato al convegno 2011 ACM Multimedia Conference, MM'11 and Co-Located Workshops - 2011 Joint ACM Workshop on Human Gesture and Behavior Understanding, J-HGBU'11 tenutosi a Scottsdale, Arizona, USA nel Nov 28 - Dec 1 2011 [10.1145/2072572.2072590].

3DPes: 3D People Dataset for Surveillance and Forensics

BALTIERI, DAVIDE;VEZZANI, Roberto;CUCCHIARA, Rita
2011-01-01

Abstract

The interest of the research community in creating reference datasets for performance analysis is always very high. Although new datasets, collecting large amounts of video footage are spreading in surveillance and forensics, few bench-marks with annotation data are available for testing specific tasks and especially for 3D/multi-view analysis. In this paper we present 3DPeS, a new dataset for 3D/multi- view surveillance and forensic applications. This has been designed for discussing and evaluating research results in people re-identification and other related activities (people detection, people segmentation and people tracking). The new assessed version of the dataset contains hundreds of video sequences of 200 people taken from a multi-camera distributed surveillance system over several days, with different light conditions; each person is detected multiple times and from different points of view. In surveillance scenarios, the dataset can be exploited to evaluate people reacquisition, 3D body models and people activity reconstruction algorithms. In forensics it can be adopted too, by relaxing some constraints (e.g. real time) and neglecting some information (e.g. calibration). Some results on this new dataset are presented using state of the art methods for people re-identification as a benchmark for future comparisons.
2011 ACM Multimedia Conference, MM'11 and Co-Located Workshops - 2011 Joint ACM Workshop on Human Gesture and Behavior Understanding, J-HGBU'11
Scottsdale, Arizona, USA
Nov 28 - Dec 1 2011
1
59
64
Baltieri, Davide; Vezzani, Roberto; Cucchiara, Rita
3DPes: 3D People Dataset for Surveillance and Forensics / Baltieri, Davide; Vezzani, Roberto; Cucchiara, Rita. - ELETTRONICO. - 1:(2011), pp. 59-64. ((Intervento presentato al convegno 2011 ACM Multimedia Conference, MM'11 and Co-Located Workshops - 2011 Joint ACM Workshop on Human Gesture and Behavior Understanding, J-HGBU'11 tenutosi a Scottsdale, Arizona, USA nel Nov 28 - Dec 1 2011 [10.1145/2072572.2072590].
File in questo prodotto:
Non ci sono file associati a questo prodotto.
Pubblicazioni consigliate

Caricamento pubblicazioni consigliate

Licenza Creative Commons
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/701106
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 219
  • ???jsp.display-item.citation.isi??? ND
social impact