Moving cast shadow detection by exploiting multiple cues

Yang, Mau-Tsuen; Lo, Kuo-Hua; Chiang, Cheng‐Chin; Tai, Wen‐Kai

doi:10.1049/iet-ipr:20070113

Cited by 45 publications

(35 citation statements)

References 8 publications

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“…In their proposed approach, patches of each new frame were compared with the respective patches of the background model, and Gabor functions were used to detect if textural information remains the same for shadowed regions [26]. Yang et al proposed a moving cast shadow detection algorithm that combines shading, color, texture, neighborhoods, and temporal consistency in the scene [27].…”

Section: Motivation and Related Workmentioning

confidence: 99%

Fuzzy Based Shadow Removal and Integrated Boundary Detection for Video Surveillance

A¹,

Sureshkumar²

2014

Journal of Electrical Engineering and Technology

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-We present a scalable object tracking framework, which is capable of removing shadows and tracking the people. The framework consists of background subtraction, fuzzy based shadow removal and boundary tracking algorithm. This work proposes a general-purpose method that combines statistical assumptions with the object-level knowledge of moving objects, apparent objects, and shadows acquired in the processing of the previous frames. Pixels belonging to moving objects and shadows are processed differently in order to supply an object-based selective update. Experimental results demonstrate that the proposed method is able to track the object boundaries under significant shadows with noise and background clutter.

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Section: Motivation and Related Workmentioning

confidence: 99%

Fuzzy Based Shadow Removal and Integrated Boundary Detection for Video Surveillance

A¹,

Sureshkumar²

2014

Journal of Electrical Engineering and Technology

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“…For example, in [6], the authors assume hue and saturation components change within a certain limit in HSV space. In [7], multiple cues including color, luminance, and texture are applied to detect moving shadows. Another commonly used feature for shadow detection is intrinsic feature.…”

Section: Previous Studiesmentioning

confidence: 99%

Outdoor shadow detection by combining tricolor attenuation and intensity

Tian

Zhu

Tang

2012

EURASIP J. Adv. Signal Process.

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Shadow detection is of broad interest in computer vision. In this article, a new shadow detection method for single color images in outdoor scenes is proposed. Shadows attenuate pixel intensity, and the degrees of attenuation are different in the three RGB color channels. Previously, we proposed the Tricolor Attenuation Model (TAM) that describes the attenuation relationship between shadows and their non-shadow backgrounds in the three color channels. TAM can provide strong information on shadow detection; however, our previous study needs a rough segmentation as the pre-processing step and requires four thresholds. These shortcomings can be overcome by adding intensity information. This article addresses the problem of how to combine TAM and intensity and meanwhile to obtain a threshold for shadow segmentation. Simple and complicated shadow images are used to test the proposed method. The experimental results and comparisons validate its effectiveness.

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“…Yang et al [69] propose a moving cast shadow detection algorithm that combines shading, color, texture, neighborhoods and temporal consistency in the scene.…”

Section: Region-levelmentioning

confidence: 99%

Moving Cast Shadows Detection Methods for Video Surveillance Applications

Amato

Huerta

Mozerov

et al. 2012

Augmented Vision and Reality

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Moving cast shadows are a major concern in today's performance from broad range of many vision-based surveillance applications because they highly difficult the object classification task. Several shadow detection methods have been reported in the literature during the last years. They are mainly divided into two domains. One usually works with static images, whereas the second one uses image sequences, namely video content. In spite of the fact that both cases can be analogously analyzed, there is a difference in the application field. The first case, shadow detection methods can be exploited in order to obtain additional geometric and semantic cues about shape and position of its casting object ('shape from shadows') as well as the localization of the light source. While in the second one, the main purpose is usually change detection, scene matching or surveillance (usually in a background subtraction context). Shadows can in fact modify in a negative way the shape and color of the target object and therefore affect the performance of scene analysis and interpretation in many applications. This chapter wills mainly reviews shadow detection methods as well as their taxonomies related with the second case, thus aiming at those shadows which are associated with moving objects (moving shadows). IntroductionVideo Surveillance has been in our society for a long time [6,13]. It began in the twentieth century to assist prison officials in the discovery of escape methods. However, it was not until the late-twentieth century that surveillance expanded to include the security of property and people. Video surveillance is more prevalent in Europe than anywhere in the world. For instance, in the past decade, successive UK governments have installed over 2.4 million surveillance cameras (about one for every 14 people). 1 The average Londoners are estimated to have their picture recorded more than three hundred times a day 2 . Traditionally video surveillance was used to display images on monitors inspected by guards or operators. This fact has allowed the observation of an increase number of places using less people and also to perform patrolling duties from the safety of a control room. However, a single operator can only monitor a limited amount of scenes simultaneously and for a limited amount of time, because the process of manual surveillance is very time-consuming and is a really tedious task.The new breakthroughs in technology have led to a new generation of video surveillance. The current generation of video surveillance systems uses digital computing and communication technologies to improve the design of the original architecture, with the ultimate goal to create an automatic video surveillance system. Recent trends in computer vision has delved into the study of cognitive vision systems, which uses visual information to facilitate a series of tasks on sensing, understanding, reaction and communication. In other words, video surveillance systems aim to automatically identify people, objects or events of ...

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Moving cast shadow detection by exploiting multiple cues

Cited by 45 publications

References 8 publications

Fuzzy Based Shadow Removal and Integrated Boundary Detection for Video Surveillance

Fuzzy Based Shadow Removal and Integrated Boundary Detection for Video Surveillance

Outdoor shadow detection by combining tricolor attenuation and intensity

Moving Cast Shadows Detection Methods for Video Surveillance Applications

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