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Abandoned Object Detection in Video-Surveillance: Survey and Comparison

Author
Luna, Eva; San Miguel Avedillo, Juan Carlosuntranslated; Ortego Hernández, Diego; Martínez Sánchez, José Maríauntranslated
Entity
UAM. Departamento de Tecnología Electrónica y de las Comunicaciones
Publisher
MDPI
Date
2018-12
Citation
10.3390/s18124290
Sensors 18.12(2018): art. 4290
 
 
 
ISSN
1424-8220
DOI
10.3390/s18124290
Funded by
We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research This work was partially supported by the Spanish Government (TEC2014-53176-R HAVideo)
Project
Gobierno de España. TEC2014-53176-R HAVideo
Editor's Version
https://doi.org/10.3390/s18124087
Subjects
Foreground segmentation; Stationary object detection; Pedestrian detection; Abandoned object; Survey; Video-surveillance; Telecomunicaciones
URI
http://hdl.handle.net/10486/689323
Rights
© 2018 by the authors

Abstract

During the last few years, abandoned object detection has emerged as a hot topic in the video-surveillance community. As a consequence, a myriad of systems has been proposed for automatic monitoring of public and private places, while addressing several challenges affecting detection performance. Due to the complexity of these systems, researchers often address independently the different analysis stages such as foreground segmentation, stationary object detection, and abandonment validation. Despite the improvements achieved for each stage, the advances are rarely applied to the full pipeline, and therefore, the impact of each stage of improvement on the overall system performance has not been studied. In this paper, we formalize the framework employed by systems for abandoned object detection and provide an extensive review of state-of-the-art approaches for each stage. We also build a multi-configuration system allowing one to select a range of alternatives for each stage with the objective of determining the combination achieving the best performance. This multi-configuration is made available online to the research community. We perform an extensive evaluation by gathering a heterogeneous dataset from existing data. Such a dataset allows considering multiple and different scenarios, whereas presenting various challenges such as illumination changes, shadows, and a high density of moving objects, unlike existing literature focusing on a few sequences. The experimental results identify the most effective configurations and highlight design choices favoring robustness to errors. Moreover, we validated such an optimal configuration on additional datasets not previously considered. We conclude the paper by discussing open research challenges arising from the experimental comparison
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Google™ Scholar:Luna, Eva - San Miguel Avedillo, Juan Carlos - Ortego Hernández, Diego - Martínez Sánchez, José María

This item appears in the following Collection(s)

  • Producción científica en acceso abierto de la UAM [17129]

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All the documents from Biblos-e Archivo are protected by copyrights. Some rights reserved.
Universidad Autónoma de Madrid. Biblioteca
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