Patch-Based Background Initialization in Heavily Cluttered Video

Colombari, A.; Fusiello, Andrea

doi:10.1109/tip.2009.2038652

Cited by 38 publications

(29 citation statements)

References 23 publications

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“…However, it can not cope with camouflages and shadows. Another solution to bootstrapping problem is presented by colombari et al in [26], where a patch-based technique exploits both spatial and temporal consistency of the static background.…”

Section: State Of the Artmentioning

confidence: 99%

Exploiting multiple cues in motion segmentation based on background subtraction

et al. 2013

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This paper presents a novel algorithm for mobile-object segmentation from static background scenes, which is both robust and accurate under most of the common problems found in motion segmentation. In our first contribution, a case analysis of motion segmentation errors is presented taking into account the inaccuracies associated with different cues, namely colour, edge and intensity. Our second contribution is an hybrid architecture which copes with the main issues observed in the case analysis by fusing the knowledge from the aforementioned three cues and a temporal difference algorithm. On one hand, we enhance the colour and edge models to solve not only global and local illumination changes (i.e. shadows and highlights) but also the camouflage in intensity. In addition, local information is also exploited to solve the camouflage in chroma. On the other hand, the intensity cue is applied when colour and edge cues are not available because their values are beyond the dynamic range. Additionally, temporal difference scheme is included to segment motion where those three cues can not be reliably computed, for example in those background regions not visible during the training period. Lastly, our approach is extended for handling ghost detection. The proposed method obtains very accurate and robust motion segmentation results in multiple indoor and outdoor scenarios, while outperforming the most-referred stateof-art approaches.

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Section: State Of the Artmentioning

confidence: 99%

Exploiting multiple cues in motion segmentation based on background subtraction

et al. 2013

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“…Six bootstrapping video sequences, selected from three benchmark datasets, namely, ATON (http://cvrr.ucsd.edu/aton/shadow/index. html), PETS2006 (http://www.cvg.rdg.ac.uk/PETS2006/ data.html), and BPI [24], are used in this study, which are listed and categorized in Table 1. In Table 1, the six bootstrapping video sequences are categorized as jiggled capture, shadow effect, and heavy clutter.…”

Section: Resultsmentioning

confidence: 99%

“…Recently, the background subtraction methods focused on background initialization for bootstrapping video sequences [19][20][21][22][23][24], in which a training duration without foreground objects is not available in some cluttered environments [3,19]. That is, background initialization for bootstrapping video sequences can be defined as follows: given a video sequence captured by a stationary camera, in which the background is occluded by some foreground objects in each frame of the video sequence, the aim is to estimate a background frame without foreground objects [22,24].…”

Section: Introductionmentioning

confidence: 99%

“…That is, background initialization for bootstrapping video sequences can be defined as follows: given a video sequence captured by a stationary camera, in which the background is occluded by some foreground objects in each frame of the video sequence, the aim is to estimate a background frame without foreground objects [22,24]. Background initialization for bootstrapping video sequences (or simply background initialization) is widely used in the intelligent video surveillance systems for monitoring crowded infrastructures, such as banks, subway, airports, and lobby.…”

Section: Introductionmentioning

confidence: 99%

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Background initialization and foreground segmentation for bootstrapping video sequences

Hsiao

Leou

2013

J Image Video Proc

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In this study, an effective background initialization and foreground segmentation approach for bootstrapping video sequences is proposed. First, a modified block representation approach is used to classify each block of the current video frame into one of four categories, namely, "background," "still object," "illumination change," and "moving object." Then, a new background updating scheme is developed, in which a side-match measure is used to determine whether the background is exposed. Finally, using the edge information, an improved noise removal and shadow suppression procedure with two morphological operations is adopted to enhance the final segmented foreground. Based on the experimental results obtained in this study, as compared with three comparison approaches, the proposed approach produces better background initialization and foreground segmentation results.

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“…However, it cannot cope with camouflages and shadows. Another solution to bootstrapping problem is presented by Colombari et al in [11], where a patch-based technique exploits both spatial and temporal consistency of the static background.…”

Section: Methods For Moving Region Extractionmentioning

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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Patch-Based Background Initialization in Heavily Cluttered Video

Cited by 38 publications

References 23 publications

Exploiting multiple cues in motion segmentation based on background subtraction

Exploiting multiple cues in motion segmentation based on background subtraction

Background initialization and foreground segmentation for bootstrapping video sequences

Moving Cast Shadows Detection Methods for Video Surveillance Applications

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