Human Cast Shadow Elimination Method Based on Orientation Information Measures

Bi, Sheng; Liang, Dequn; Shen, Xiaoyan; Wang, Qiang

doi:10.1109/ical.2007.4338821

Cited by 7 publications

(6 citation statements)

References 9 publications

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“…Bi et al [34] introduced a shadow detection method based on human body geometrical existence and its approximate location. In the first step, the human body shape property is analysed and used to determine the location of cast shadow.…”

Section: Shape Based Methodsmentioning

confidence: 99%

An evaluation of moving shadow detection techniques

2016

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Shadows of moving objects may cause serious problems in many computer vision applications, including object tracking and object recognition. In common object detection systems, due to having similar characteristics, shadows can be easily misclassified as either part of moving objects or independent moving objects. To deal with the problem of misclassifying shadows as foreground, various methods have been introduced. This paper addresses the main problematic situations associated with shadows and provides a comprehensive performance comparison on up-todate methods that have been proposed to tackle these problems. The evaluation is carried out using benchmark datasets that have been selected and modified to suit the purpose. This survey suggests the ways of selecting shadow detection methods under different scenarios.

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Section: Shape Based Methodsmentioning

confidence: 99%

An evaluation of moving shadow detection techniques

2016

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“…In the presented solution it was decided to use a heuristic approach, in a manner similar to that proposed in [2,17,21]. This section describes the designed algorithm.…”

Section: The Proposed Algorithmmentioning

confidence: 99%

“…The second group consists of methods designed specifically for processing of a video stream. They can be divided into operating on greyscale [2,7,8,17,19,21] and colour [11,13,14,15] stream.…”

Section: Introductionmentioning

confidence: 99%

Shadow Removal for Greyscale Video Sequences

Kryjak

2012

Image Processing & Communications

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The paper presents a shadow detection and elimination algorithm designed for greyscale video sequences. The paper proposes: an automatic method for determining the binarization threshold on the basis of the object edge analysis, division of areas identified as potential shadow using a rectangular grid, analyzing the similarities between the current frame and the background model performed in areas and analyzing the potential shadows areas position relative to the position of areas identified as a true object. The algorithm was designed to eliminate shadows casted by people in video surveillance sequences. The obtained results show the usefulness of the proposed solution.

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“…Hence using only color information for shadow detection is unreliable. Recent literature shows a trend towards the use of texture information or region level processing for the shadow detection [3,4,5,6]. In [10] authors have proposed shadow detection algorithm using color information and constraints from physical knowledge.…”

Section: Introductionmentioning

confidence: 99%

Shadow detection for moving humans using gradient-based background subtraction

Shoaib

Dragon

Östermann

2009

2009 IEEE International Conference on Acoustics, Speech and Signal Processing

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Cast shadows cause serious problems in the functionality of vision-based applications, such as video surveillance, traffic monitoring and various other applications. Accurate detection and removal of cast shadows is a challenging task. Common shadow detection techniques normally use color information, which is not a reliable base in every scenario. This paper presents a novel scheme for real time detection of cast shadows using contour like structures of objects, which are obtained by gradient-based background subtraction. The scheme does not use any color information. Two basic rules are followed for shadow detection. The first rule is that shadows do not change the texture of the background. The second rule is a cast shadow lies outside the boundary of an object and has a relatively small common boundary with the object. Experimental results show the performance of the proposed scheme. Objective evaluation shows that the algorithm classifies 90 percent of the pixels of the objects and their shadow correctly.

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Human Cast Shadow Elimination Method Based on Orientation Information Measures

Cited by 7 publications

References 9 publications

An evaluation of moving shadow detection techniques

An evaluation of moving shadow detection techniques

Shadow Removal for Greyscale Video Sequences

Shadow detection for moving humans using gradient-based background subtraction

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