Separation of moving objects and their shadows, and application to tracking of loci in the monitoring images

Sonoda, Yorinobu; Ogata, Takayuki

doi:10.1109/icosp.1998.770848

Cited by 23 publications

(15 citation statements)

References 2 publications

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“…The method in [8] models the values of shadowed pixels using a transformation matrix which indicates the amount of brightness change a pixel undergoes in the presence of a cast shadow. Another method which uses geometry to find shadowed regions is outlined in [9]. It produces height estimates of objects using their shadow positions and size by applying geometric reasoning.…”

Section: Related Workmentioning

confidence: 99%

Moving Shadow Detection with Low- and Mid-Level Reasoning

Joshi

Atev

Masoud

et al. 2007

Proceedings 2007 IEEE International Conference on Robotics and Automation

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Abstract-In this paper, we propose a multi-level shadow identification scheme which is generally applicable without restrictions on the number of light sources, illumination conditions, surface orientations, and object sizes. In the first level, we use a background segmentation technique to identify foreground regions which include moving shadows. In the second step, pixel-based decisions are made by comparing the current frame with the background model to distinguish between shadows and actual foreground. In the third step, this result is improved using blob-level reasoning which works on geometric constraints of identified shadow and foreground blobs. Results on various indoor and outdoor sequences under different illumination conditions show the success of the proposed approach.

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Section: Related Workmentioning

confidence: 99%

Moving Shadow Detection with Low- and Mid-Level Reasoning

Joshi

Atev

Masoud

et al. 2007

Proceedings 2007 IEEE International Conference on Robotics and Automation

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“…Thus, penumbras usually appear and they may be quite large, forming insignificant shadows. In the literature, much work has been done on significant shadow detection [3], [6], [7], [10], [14], [17], [19], [21]. However, less work [4], [6], [20] can be found on insignificant shadow detection, which is more challenging than the former.…”

Section: Insignificant Shadow Detection For Video Segmentation I Intmentioning

confidence: 99%

Insignificant shadow detection for video segmentation

Liu

et al. 2005

IEEE Trans. Circuits Syst. Video Technol.

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Abstract-To prevent moving cast shadows from being misunderstood as part of moving objects in change detection based video segmentation, this paper proposes a novel approach to the cast shadow detection based on the edge and region information in multiple frames. First, an initial change detection mask containing moving objects and cast shadows is obtained. Then a Canny edge map is generated. After that, the shadow region is detected and removed through multiframe integration, edge matching, and region growing. Finally, a post processing procedure is used to eliminate noise and tune the boundaries of the objects. Our approach can be used for video segmentation in indoor environment. The experimental results demonstrate its good performance.

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“…Shadow points are separated using the fact that shadows are on the ground plane, whereas foreground objects are not. Another proposed method uses geometry to find shadowed regions [8]. It produces height estimates of objects using their shadow position and size by applying geometry.…”

Section: Introductionmentioning

confidence: 99%

Moving Shadow Detection in Video Using Cepstrum

Cogun

Çetin

2013

International Journal of Advanced Robotic Systems

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Moving shadows constitute problems in various applications such as image segmentation and object tracking. The main cause of these problems is the misclassification of the shadow pixels as target pixels. Therefore, the use of an accurate and reliable shadow detection method is essential to realize intelligent video processing applications. In this paper, a cepstrum-based method for moving shadow detection is presented. The proposed method is tested on outdoor and indoor video sequences using well-known benchmark test sets. To show the improvements over previous approaches, quantitative metrics are introduced and comparisons based on these metrics are made.

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Separation of moving objects and their shadows, and application to tracking of loci in the monitoring images

Cited by 23 publications

References 2 publications

Moving Shadow Detection with Low- and Mid-Level Reasoning

Moving Shadow Detection with Low- and Mid-Level Reasoning

Insignificant shadow detection for video segmentation

Moving Shadow Detection in Video Using Cepstrum

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