Multi-feature stationary foreground detection for crowded video-surveillance

Ortego, Diego; SanMiguel, Juan C.

doi:10.1109/icip.2014.7025486

Cited by 10 publications

(9 citation statements)

References 21 publications

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“…Another single Gaussian-based modeling strategy that has been used by several authors [57,65,100] is the one proposed in [118], which is able to deal with complex camera noise and a large variety of slow environmental light changes. Additionally, by modeling not at pixel level but in a spatial neighborhood around each pixel, it provides robust detections in a wider variety of situations (e.g.…”

Section: Single Gaussian Models (Sgm)mentioning

confidence: 99%

Detection of stationary foreground objects: A survey

Cuevas

Martínez

García

2016

Computer Vision and Image Understanding

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Section: Single Gaussian Models (Sgm)mentioning

confidence: 99%

Detection of stationary foreground objects: A survey

Cuevas

Martínez

García

2016

Computer Vision and Image Understanding

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“…The proposed approach is compared with four state-of-the-art approaches: temporal accumulation for single [6] and multiple [9] features, temporal sampling [10] and dual background [11]. These approaches are tested both in short-term and longterm sequences.…”

Section: B Comparative Evaluationmentioning

confidence: 99%

“…The proposed approach detects all objects without false positives, thus demonstrating the robustness in crowded situations such as AB M, AB H and PV H, where illumination changes and cast shadows take place. The state-of-the-art approaches have serious difficulties to cope with crowds, thus producing false detections in all cases except [9].…”

Section: B Comparative Evaluationmentioning

confidence: 99%

“…Whilst temporal accumulation of foreground masks [6] is widely used, post-processing [7][8] and combination [9] of additional features are required to address BS limitations in presence of crowds and illumination challenges. Temporal sampling of foreground and motion masks enables operation in complex scenes [10], being the sample selection crucial for the detection accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Long-Term Stationary Object Detection Based on Spatio-Temporal Change Detection

Ortego

SanMiguel

Martinez

2015

IEEE Signal Process. Lett.

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Esta es la versión de autor del artículo publicado en: This is an author produced version of a paper published in: Abstract-We present a block-wise approach to detect stationary objects based on spatio-temporal change detection. First, block candidates are extracted by filtering out consecutive blocks containing moving objects. Then, an online clustering approach groups similar blocks at each spatial location over time via statistical variation of pixel ratios. The stability changes are identified by analyzing the relationships between the most repeated clusters at regular sampling instants. Finally, stationary objects are detected as those stability changes that exceed an alarm time and have not been visualized before. Unlike previous approaches making use of Background Subtraction, the proposed approach does not require foreground segmentation and provides robustness to illumination changes, crowds and intermittent object motion. The experiments over an heterogeneous dataset demonstrate the ability of the proposed approach for short-and long-term operation while overcoming challenging issues.

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“…One year later, Bayona proposed one static foreground detection technique based on a sub-sampling scheme that outperformed other efforts mentioned in his survey. A succession of improvements has been reported in [3] and [4]. Although the stationary foreground detection issue *Correspondence: alp@ita.br 1 Instituto Tecnológico de Aeronáutica (ITA), Praça Mal.…”

Section: Introductionmentioning

confidence: 99%

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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Multi-feature stationary foreground detection for crowded video-surveillance

Cited by 10 publications

References 21 publications

Detection of stationary foreground objects: A survey

Detection of stationary foreground objects: A survey

Long-Term Stationary Object Detection Based on Spatio-Temporal Change Detection

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

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