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Erratum: Liu, Y.-Z.; Jiang, X.-K.; Cao, W.-F.; Sun, J.-Y.; Gao, F. Detection of Thrombin Based on Fluorescence Energy Transfer between Semiconducting Polymer Dots and BHQ-Labelled Aptamers. Sensors 2018, 18, 589
Open AccessArticle

Abandoned Object Detection in Video-Surveillance: Survey and Comparison

Video Processing and Understanding Lab, Universidad Autónoma de Madrid, 28049 Madrid, Spain
Author to whom correspondence should be addressed.
Sensors 2018, 18(12), 4290;
Received: 7 November 2018 / Revised: 30 November 2018 / Accepted: 2 December 2018 / Published: 5 December 2018
(This article belongs to the Section Intelligent Sensors)
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. View Full-Text
Keywords: foreground segmentation; stationary object detection; pedestrian detection; abandoned object; survey; video-surveillance foreground segmentation; stationary object detection; pedestrian detection; abandoned object; survey; video-surveillance
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Luna, E.; San Miguel, J.C.; Ortego, D.; Martínez, J.M. Abandoned Object Detection in Video-Surveillance: Survey and Comparison. Sensors 2018, 18, 4290.

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