Logo des Repositoriums
 

Human-centered evaluation of anomalous events detection in crowded environments

dc.contributor.authorGiulia Orrù, Elia Porcedda
dc.contributor.editorDamer, Naser
dc.contributor.editorGomez-Barrero, Marta
dc.contributor.editorRaja, Kiran
dc.contributor.editorRathgeb, Christian
dc.contributor.editorSequeira, Ana F.
dc.contributor.editorTodisco, Massimiliano
dc.contributor.editorUhl, Andreas
dc.date.accessioned2023-12-12T10:46:48Z
dc.date.available2023-12-12T10:46:48Z
dc.date.issued2023
dc.description.abstractAnomaly detection in crowd analysis refers to the ability to detect events and people’s behaviours that deviate from normality. Anomaly detection techniques are developed to support human operators in various monitoring and investigation activities. So far, the anomaly detectors' performance evaluation derives from the rate of correctly classified individual frames, according to the labels given by the annotator. This evaluation does not make the system's performance appreciable, especially from a human operator viewpoint. In this paper, we propose a novel evaluation approach called ``Trigger-Level evaluation'' that is shown to be human-centered and closer to the user's perception of the system's performance. In particular, we define two new performance metrics to aid the evaluation of the usability of anomaly detectors in real-time.en
dc.identifier.isbn978-3-88579-733-3
dc.identifier.issn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/43279
dc.language.isoen
dc.pubPlaceBonn
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofBIOSIG 2023
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-339
dc.subjectGait and Action Recognition
dc.titleHuman-centered evaluation of anomalous events detection in crowded environmentsen
dc.typeText/Conference Paper
mci.conference.date20.-22. September 2023
mci.conference.locationDarmstadt
mci.conference.sessiontitleFurther Conference Contributions
mci.reference.pages305-314

Dateien

Originalbündel
1 - 1 von 1
Lade...
Vorschaubild
Name:
LNI_041.pdf
Größe:
1.54 MB
Format:
Adobe Portable Document Format