Fast and robust shadow detection in videoconference applications

Schreer, Oliver; Feldmann, Ingo; Golz, U.; Kauff, Peter

doi:10.1109/viprom.2002.1026685

Cited by 54 publications

(36 citation statements)

References 3 publications

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“…These methods are mostly based on the fact that shadows change the luminance but color little. Several researches proposed shadow detection methods based on the RGB, HSV and YUV [29] color spaces. Most of research assumes that a shadow belongs to a dark region.…”

Section: Shadow Removalmentioning

confidence: 99%

A Video-Based Object Detection System for Improving Safety at Level Crossings

Fakhfakh¹,

Khoudour²,

El-Koursi³

et al. 2011

TOTJ

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Section: Shadow Removalmentioning

confidence: 99%

A Video-Based Object Detection System for Improving Safety at Level Crossings

Fakhfakh¹,

Khoudour²,

El-Koursi³

et al. 2011

TOTJ

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“…In [2], Cucchiara et al use the hypothesis that shadows reduce surface brightness and saturation while maintaining hue properties in the HSV colour space. While Schreer et al [18] adopt the YUV colour space. In [5,8], Horprasert et al and Kim et al build a model in the RGB colour space to express normalised luminance variation and chromaticity distortions.…”

Section: State Of the Art And Our Contributionsmentioning

confidence: 99%

Detection and removal of chromatic moving shadows in surveillance scenarios

Huerta¹,

Holte

Moeslund

et al. 2009

2009 IEEE 12th International Conference on Computer Vision

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Segmentation in the surveillance domain has to deal with shadows to avoid distortions when detecting moving objects. Most segmentation approaches dealing with shadow detection are typically restricted to penumbra shadows. Therefore, such techniques cannot cope well with umbra shadows. Consequently, umbra shadows are usually detected as part of moving objects. In this paper we present a novel technique based on gradient and colour models for separating chromatic moving cast shadows from detected moving objects. Firstly, both a chromatic invariant colour cone model and an invariant gradient model are built to perform automatic segmentation while detecting potential shadows. In a second step, regions corresponding to potential shadows are grouped by considering "a bluish effect" and an edge partitioning. Lastly, (i) temporal similarities between textures and (ii) spatial similarities between chrominance angle and brightness distortions are analysed for all potential shadow regions in order to finally identify umbra shadows. Unlike other approaches, our method does not make any a-priori assumptions about camera location, surface geometries, surface textures, shapes and types of shadows, objects, and background. Experimental results show the performance and accuracy of our approach in different shadowed materials and illumination conditions.

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“…The chromaticity-based methods are based on the assumption that shadow pixels become darker as they are blocked from the illumination source but their chromaticity remains the same as the corresponding background pixels, which referred to as color constancy [3] or linear attenuation [4]. These properties have been employed in different colors spaces like HSV [5], YUV [6], normalized RGB [7] and c1c2c3 [8]. Most of these methods are simple to perform and their processing time is the fastest comparatively.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Cast Shadow Detection with Combined Texture and Color Models

Liu¹,

Zhu²

2014

IJET

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Abstract-Cast shadows of moving foreground objects can cause miss tracking problem in object detection and tracking, thus shadow detection is an important step used after a moving foreground object is detected. Most of current methods have a significant trade-off between the shadow detection rate and the shadow discrimination rate. In this paper, an effective and adaptive method with combined texture and color models is proposed in order to achieve good shadow detection rate and shadow discrimination rate as well. Firstly, Scale Invariant Local Ternary Pattern (SILTP) is used to select a candidate shadow region. Then HSV color model is employed to detect a new candidate shadow region by using maximum likelihood estimation (MLE) to estimate the thresholds of HSV color model adaptively. Finally the two regions are combined by logical operation and a new shadow region can be obtained. Our experimental results show that the proposed method achieves a better performance in both shadow detection rate and discrimination rate compared to the other current methods. Moreover, the proposed method runs at 100 frames per second and is suitable for the real-time detection and tracking.

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Fast and robust shadow detection in videoconference applications

Cited by 54 publications

References 3 publications

A Video-Based Object Detection System for Improving Safety at Level Crossings

A Video-Based Object Detection System for Improving Safety at Level Crossings

Detection and removal of chromatic moving shadows in surveillance scenarios

An Adaptive Cast Shadow Detection with Combined Texture and Color Models

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