Detection of Stationary Foreground Objects Using Multiple Nonparametric Background-Foreground Models on a Finite State Machine

Cuevas, Carlos; Martínez, Raquel; Berjón, Daniel; García, Narciso

doi:10.1109/tip.2016.2642779

Cited by 26 publications

(10 citation statements)

References 58 publications

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“…For the Static foreground detection stage, we have included the most common approach employing two background models (DBM) [77] and the recent extension to three models (TBM) [59]. Moreover, we also have integrated the foreground accumulation approach (ACC) [63].…”

Section: Experimental Methodologymentioning

confidence: 99%

“…The latter applies a standard dual background model followed by a set of filters to verify geometrical properties, distinguish vehicles from any other object, and track them to improve the persistence modeling. Recently, the dual background model was extended to a triple background model [59] by including a medium-term model where, following the same ideas of [46], an FSM and accumulation and thresholding were used to report the final alarms.…”

Section: Stages Of Abandoned Object Detectionmentioning

confidence: 99%

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Abandoned Object Detection in Video-Surveillance: Survey and Comparison

Luna

SanMiguel

Ortego

et al. 2018

Sensors

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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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Section: Experimental Methodologymentioning

confidence: 99%

Section: Stages Of Abandoned Object Detectionmentioning

confidence: 99%

Abandoned Object Detection in Video-Surveillance: Survey and Comparison

Luna

SanMiguel

Ortego

et al. 2018

Sensors

View full text Add to dashboard Cite

show abstract

“…Wahyono et al [13] and Filonenko et al [14] detect stationary objects using the difference of a reference background and the current background. The triple background model presented by Cuevas et al [15] solves the problem of longterm abandoned object detection and occlusion by adding a long-term model with no background absorption. However, all the studies based on multiple background models focus on the detection of abandoned objects and do not address the problem of distinguishing whether a candidate stationary object is an abandoned object or a stolen object.…”

Section: Related Workmentioning

confidence: 99%

Detection of Abandoned and Stolen Objects Based on Dual Background Model and Mask R-CNN

Park

Joo

2020

IEEE Access

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Dual background models have been widely used for detecting stationary objects in video surveillance systems. However, there is a problem that both abandoned and stolen objects are equally detected as stationary objects, making it difficult to distinguish them. Another problem is the ghost region created by shadow shift or light changes, which makes the discrimination issue more complicated. In this paper, we present an efficient method to distinguish abandoned objects, stolen objects, and ghost regions in the surveillance video. This method contains two main strategies: the first one is the dual background model for extracting candidate stationary objects, the second one is object segmentation based on mask regions with CNN features (Mask R-CNN) for providing the object mask information. The basic idea is: given a candidate stationary object from the background model, it is checked whether a corresponding segmented object exists in the current video frame or the previous background frame to take into account the current and past situations. And the final state of the candidate stationary object is determined by considering various situations through the comparative analysis technique presented in this paper. The proposed algorithm has qualitatively experimented with our own dataset focusing on the discrimination issue, which generated satisfactory results. Therefore, it is expected to be widely applied to automatic detection of stolen objects as well as abandoned objects in open environments such as exhibition halls and public parks where existing intrusion detection-based security services are difficult to be deployed. INDEX TERMS Abandoned object detection, stolen object detection, ghost region, dual background model, mask R-CNN.

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“…In the triple background subtraction model, short-term, medium-term, and long-term background subtraction models are used [9]. Since the stationary objects are never absorbed into the background and always remain as the foreground in the long-term model, it is very effective for the detection of the long-term abandoned objects that have been abandoned for quite a long time.…”

Section: Related Workmentioning

confidence: 99%

Robust Real-time Detection of Abandoned Objects using a Dual Background Model

2020

KSII TIIS

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Detection of abandoned objects for smart video surveillance should be robust and accurate in various situations with low computational costs. This paper presents a new algorithm for abandoned object detection based on the dual background model. Through the template registration of a candidate stationary object and presence authentication methods presented in this paper, we can handle some complex cases such as occlusions, illumination changes, long-term abandonment, and owner's re-attendance as well as general detection of abandoned objects. The proposed algorithm also analyzes video frames at specific intervals rather than consecutive video frames to reduce the computational overhead. For performance evaluation, we experimented with the algorithm using the well-known PETS2006, ABODA datasets, and our video dataset in a live streaming environment, which shows that the proposed algorithm works well in various situations

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Detection of Stationary Foreground Objects Using Multiple Nonparametric Background-Foreground Models on a Finite State Machine

Cited by 26 publications

References 58 publications

Abandoned Object Detection in Video-Surveillance: Survey and Comparison

Abandoned Object Detection in Video-Surveillance: Survey and Comparison

Detection of Abandoned and Stolen Objects Based on Dual Background Model and Mask R-CNN

Robust Real-time Detection of Abandoned Objects using a Dual Background Model

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