A survey of cast shadow detection algorithms

Al-Najdawi, Nijad; Bez, Helmut E.; Singhai, Jyoti; Edirisinghe, Eran A.

doi:10.1016/j.patrec.2011.12.013

Cited by 95 publications

(66 citation statements)

References 26 publications

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“…In other survey, Sanin et al [67] followed the work done in [66] but with comparing recent publications and using a more comprehensive set of test sequences. Recently, in their survey, Al-Najdawi et al [68] brought an exhaustive comparison on cast shadow detection algorithms based on object/environment dependency and implementation domain. Recent algorithms have been compared in a survey written by Tiwari et al [69] who categorized the shadow detection algorithms in different scenarios such indoor/outdoor scenes, fixed/moving cameras, and umbra/penumbra shadows.…”

Section: Shadowmentioning

confidence: 99%

New trends on moving object detection in video images captured by a moving camera: A survey

Yazdi

Bouwmans

2018

Computer Science Review

209

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This paper presents a survey on the latest methods of moving object detection in video sequences captured by a moving camera. Although many researches and excellent works have reviewed the methods of object detection and background subtraction for a fixed camera, there is no survey which presents a complete review of the existing different methods in the case of moving camera. Most methods in this field can be classified into four categories: modelling based background subtraction, trajectory classification, low rank and sparse matrix decomposition, and object tracking. We discuss in details each category and present the main methods which proposed improvements in the general concept of the techniques. We also present challenges and main concerns in this field as well as performance metrics and some benchmark databases available to evaluate the performance of different moving object detection algorithms.

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Section: Shadowmentioning

confidence: 99%

New trends on moving object detection in video images captured by a moving camera: A survey

Yazdi

Bouwmans

2018

Computer Science Review

209

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“…23,24 2.3 Occlusion Management Except when the camera is located above the road, with perpendicular viewing to the road surface, when vehicles are close, they partially occlude one another and correct counting is difficult. The problem becomes harder when the occlusion occurs as soon as the vehicles appear in the field of view.…”

Section: Shadow Removalmentioning

confidence: 99%

Automatic vehicle counting system for traffic monitoring

Crouzil

Khoudour

Valiere

et al. 2016

J. Electron. Imaging

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“…However, these shadow detection and removal algorithms are difficult to implement and require a significant amount of computation time, which is an important issue for real-time field applications. Moreover, these algorithms provide poor shadow removal for outdoor scenes (Al-Najdawi et al 2012). Therefore, a simple and effective shadow detection and removal algorithm is needed for real-time weed detection and control application in an agricultural field environment.…”

Section: Introductionmentioning

confidence: 99%

Improved vegetation segmentation with ground shadow removal using an HDR camera

2017

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A vision-based weed control robot for agricultural field application requires robust vegetation segmentation. The output of vegetation segmentation is the fundamental element in the subsequent process of weed and crop discrimination as well as weed control. There are two challenging issues for robust vegetation segmentation under agricultural field conditions: (1) to overcome strongly varying natural illumination; (2) to avoid the influence of shadows under direct sunlight conditions. A way to resolve the issue of varying natural illumination is to use high dynamic range (HDR) camera technology. HDR cameras, however, do not resolve the shadow issue. In many cases, shadows tend to be classified during the segmentation as part of the foreground, i.e., vegetation regions. This study proposes an algorithm for ground shadow detection and removal, which is based on color space conversion and a multilevel threshold, and assesses the advantage of using this algorithm in vegetation segmentation under natural illumination conditions in an agricultural field. Applying shadow removal improved the performance of vegetation segmentation with an average improvement of 20, 4.4, and 13.5% in precision, specificity and modified accuracy, respectively. The average processing time for vegetation segmentation with shadow removal was 0.46 s, which is acceptable for real-time application (\1 s required). The proposed ground shadow detection and removal method enhances the performance of vegetation segmentation under natural illumination conditions in the field and is feasible for real-time field applications.

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A survey of cast shadow detection algorithms

Cited by 95 publications

References 26 publications

New trends on moving object detection in video images captured by a moving camera: A survey

New trends on moving object detection in video images captured by a moving camera: A survey

Automatic vehicle counting system for traffic monitoring

Improved vegetation segmentation with ground shadow removal using an HDR camera

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