Teklehaymanot, F. K. ; Muma, M. ; Zoubir, A. M. (2017)
Adaptive Diffusion-Based Track Assisted Multi-Object Labeling in Distributed Camera Networks.
25th European Signal Processing Conference (EUSIPCO). Kos, Greece (28.08.-02.09.2017)
doi: 10.23919/EUSIPCO.2017.8081620
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
The tracking and labeling of multiple objects in multiple cameras is a fundamental task in applications such as video surveillance, autonomous driving, and sports analysis. In an ad-hoc multi-camera network without a fusion center nodes can benefit from local cooperation to solve signal processing tasks, such as distributed image enhancement. A crucial first step for the successful cooperation of neighboring nodes is to answer the question: Who observes what?. In this paper, an adaptive algorithm is proposed that enables cameras with different view points to assign the same identity to the same object across time frames without assuming the availability of camera calibration information or requiring the registration of camera views. Information which is extracted directly from the videos and is shared in the network via a diffusion algorithm is exploited to jointly solve multi-object tracking and labeling problems in a multi-camera network. A real-data use case of pedestrian labeling is provided, which demonstrates that a high labeling accuracy can be achieved in a multi-object multi-camera setup with low video resolution and frequent object occlusions.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2017 |
| Autor(en): | Teklehaymanot, F. K. ; Muma, M. ; Zoubir, A. M. |
| Art des Eintrags: | Bibliographie |
| Titel: | Adaptive Diffusion-Based Track Assisted Multi-Object Labeling in Distributed Camera Networks |
| Sprache: | Englisch |
| Publikationsjahr: | 26 Oktober 2017 |
| Verlag: | IEEE |
| Buchtitel: | 25th European Signal Processing Conference - EUSIPCO 2017 |
| Veranstaltungstitel: | 25th European Signal Processing Conference (EUSIPCO) |
| Veranstaltungsort: | Kos, Greece |
| Veranstaltungsdatum: | 28.08.-02.09.2017 |
| DOI: | 10.23919/EUSIPCO.2017.8081620 |
| Kurzbeschreibung (Abstract): | The tracking and labeling of multiple objects in multiple cameras is a fundamental task in applications such as video surveillance, autonomous driving, and sports analysis. In an ad-hoc multi-camera network without a fusion center nodes can benefit from local cooperation to solve signal processing tasks, such as distributed image enhancement. A crucial first step for the successful cooperation of neighboring nodes is to answer the question: Who observes what?. In this paper, an adaptive algorithm is proposed that enables cameras with different view points to assign the same identity to the same object across time frames without assuming the availability of camera calibration information or requiring the registration of camera views. Information which is extracted directly from the videos and is shared in the network via a diffusion algorithm is exploited to jointly solve multi-object tracking and labeling problems in a multi-camera network. A real-data use case of pedestrian labeling is provided, which demonstrates that a high labeling accuracy can be achieved in a multi-object multi-camera setup with low video resolution and frequent object occlusions. |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Robust Data Science 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Signalverarbeitung LOEWE LOEWE > LOEWE-Schwerpunkte LOEWE > LOEWE-Schwerpunkte > NICER – Vernetzte infrastrukturlose Kooperation zur Krisenbewältigung Exzellenzinitiative Exzellenzinitiative > Graduiertenschulen Exzellenzinitiative > Graduiertenschulen > Graduate School of Computational Engineering (CE) |
| Hinterlegungsdatum: | 15 Aug 2017 13:25 |
| Letzte Änderung: | 15 Nov 2023 10:55 |
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