Abandoned Object Detection in Video-Surveillance: Survey and Comparison
Entity
UAM. Departamento de Tecnología Electrónica y de las ComunicacionesPublisher
MDPIDate
2018-12Citation
10.3390/s18124290
Sensors 18.12(2018): art. 4290
ISSN
1424-8220DOI
10.3390/s18124290Funded 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 HAVideoEditor's Version
https://doi.org/10.3390/s18124087Subjects
Foreground segmentation; Stationary object detection; Pedestrian detection; Abandoned object; Survey; Video-surveillance; TelecomunicacionesRights
© 2018 by the authorsAbstract
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
Files in this item
Google Scholar:Luna, Eva
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San Miguel Avedillo, Juan Carlos
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Ortego Hernández, Diego
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Martínez Sánchez, José María
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