Pixel-based colour contrast for abandoned and stolen object discrimination in video surveillance

SanMiguel, Juan C.; Caro, L.; Martinez, José M.

doi:10.1049/el.2011.3160

Cited by 10 publications

(7 citation statements)

References 4 publications

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“…Our result is 3.7% more accurate than the results from [14]. We argue that this improvement is mainly due to: 1) the diversity of patch shapes that makes the histogram feature take into consideration (most of the times) suitable regions, 2) the contour feature searching for edges in the internal neighborhood of a blob and in the external neighborhood of the blob convex hull, 3) combining the SUSAN with Sobel edges in the contour feature, and 4) replacing fixed feature thresholds for dynamic ones.…”

Section: Resultssupporting

confidence: 45%

“…In this table, TP stands for the number of true classified abandoned object, FP stands for the misclassified abandoned objects, TN stands for true classified removed objects, and FN misclassified removed objects. The sixth column presents the recall (TP/(TP + FN)), the seventh column presents the accuracy ((TP + TN)/ (FP + FN)) of the proposed technique, and the last column presents the best accuracy results achieved by the creators of this dataset [14].…”

Section: Resultsmentioning

confidence: 99%

“…The Sobel [5,[7][8][9][10][11][12] and Canny [5,[13][14][15][16] operators have been employed with greater frequency for this purpose than the SUSAN edge detector [17]. While edge energy was employed in [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

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Patch-based local histograms and contour estimation for static foreground classification

Pereira

Saotome

Sampaio

2015

J Image Video Proc.

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This paper presents an approach to classify static foreground blobs in surveillance scenarios. Possible application is the detection of abandoned and removed objects. In order to classify the blobs, we developed two novel features based on the assumption that the neighborhood of a removed object is fairly continuous. In other words, there is a continuity, in the input frame, ranging from inside the corresponding blob contour to its surrounding region. Conversely, it is usual to find a discontinuity, i.e., edges, surrounding an abandoned object. We combined the two features to provide a reliable classification. In the first feature, we use several local histograms as a measure of similarity instead of previous attempts that used a single one. In the second, we developed an innovative method to quantify the ratio of the blob contour that corresponds to actual edges in the input image. A representative set of experiments shows that the proposed approach can outperform other equivalent techniques published recently.

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Section: Resultssupporting

confidence: 45%

Section: Resultsmentioning

confidence: 99%

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Patch-based local histograms and contour estimation for static foreground classification

Pereira

Saotome

Sampaio

2015

J Image Video Proc.

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“…In [118], the authors proposed a complete abandoned object detection system using a fusion hybrid approach to classify potential candidates. Another innovative method also combining edge and color information, called Pixel Color Contrast (PCC), was proposed in [119] for this aim. Figure 8 illustrates an example of the color-based approach proposed in [119].…”

Section: Stages Of Abandoned Object Detectionmentioning

confidence: 99%

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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“…With this in mind, all identification information recorded, if any, is subject to full-scale masking, or the entirety of the visual data is encrypted to prevent leakage by a third party. However, when images are recovered by a legitimate user with access rights, all of the masked images contained in the visual data will be recovered, or the entirety of the video recording will be decrypted and sent to the administrator in the form of original visual information [5][6][7][8][9]. The original footage contains recovered identification information regarding not only the target being tracked, but also the others whose images are captured on the same video, which may seriously compromise the privacy of those non-target individuals [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

A Mechanism of Masking Identification Information regarding Moving Objects Recorded on Visual Surveillance Systems by Differentially Implementing Access Permission

Park

Kim

2019

Electronics

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Video surveillance systems (VSS), used as a measure of security strengthening as well as investigation, are provided principally in heavily crowded public places. They record images of moving objects and transmit them to the control center. Typically, the recorded images are stored after being encrypted, or masked using visual obfuscations on a concerned image(s) in the identification-enabling data contained in the visual information. The stored footage is recovered to its original state by authorized users. However, the recovery entails the restoration of all information in the visual data, possibly infiltrating the privacy of the object(s) other than the one(s) whose images are requested. In particular, Artificial Intelligence Healthcare that checks the health status of an object through images has the same problem and must protect the patient's identification information. This study proposes a masking mechanism wherein the infiltration of visual data privacy on videos is minimized by limiting the objects whose images are recovered with differential use of access permission granted to the requesting users.

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Pixel-based colour contrast for abandoned and stolen object discrimination in video surveillance

Cited by 10 publications

References 4 publications

Patch-based local histograms and contour estimation for static foreground classification

Patch-based local histograms and contour estimation for static foreground classification

Abandoned Object Detection in Video-Surveillance: Survey and Comparison

A Mechanism of Masking Identification Information regarding Moving Objects Recorded on Visual Surveillance Systems by Differentially Implementing Access Permission

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