Detecting moving objects, ghosts, and shadows in video streams

Cucchiara, Rita; Grana, Costantino; Piccardi, Massimo; Prati, Andrea

doi:10.1109/tpami.2003.1233909

Cited by 1,240 publications

(770 citation statements)

References 21 publications

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“…Penumbra shadows can be characterized by low value of intensity while preserving the chromaticity of the background, i.e.achromatic shadows. Most research on detecting shadows have focused on achromatic shadows [22,12,5].…”

Section: Moving Shadow Suppressionmentioning

confidence: 99%

“…Therefore, the HSV color space has been used in some background subtraction algorithms that suppress shadows, e.g. [5]. Similarly, HSL, CIE xy spaces have the same property.…”

Section: Moving Shadow Suppressionmentioning

confidence: 99%

“…[22,12,5,31,24] In [22] both brightness and color distortions are defined using a chromatic cylinder model. By projecting an observed pixel color to the vector defined by that pixel's background value in the RGB color space (chromaticity line), the color distortion is defined as the orthogonal distance, while the projection defines the brightness distortion.…”

Section: Moving Shadow Suppressionmentioning

confidence: 99%

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Figure-Ground Segmentation—Pixel-Based

Elgammal

2011

Visual Analysis of Humans

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Background subtraction is a widely used concept to detect moving objects in videos taken from a static camera. In the last two decades several algorithms have been developed for background subtraction and were used in various important applications such as visual surveillance, sports video analysis, motion capture, etc.Various statistical approaches have been proposed to model scene background. In this chapter we review the concept and the practice in background subtraction. We discuss several basic statistical background subtraction models, including parametric Gaussian models and nonparametric models. We discuss the issue of shadow suppression, which is essential for human motion analysis applications. We also discuss approaches and tradeoffs for background maintenance. We also point out many of the recent developments in background subtraction paradigm

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Section: Moving Shadow Suppressionmentioning

confidence: 99%

“…Therefore, the HSV color space has been used in some background subtraction algorithms that suppress shadows, e.g. [5]. Similarly, HSL, CIE xy spaces have the same property.…”

Section: Moving Shadow Suppressionmentioning

confidence: 99%

Section: Moving Shadow Suppressionmentioning

confidence: 99%

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Figure-Ground Segmentation—Pixel-Based

Elgammal

2011

Visual Analysis of Humans

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“…According with the current literature, our system is based on background subtraction and models the background using statistics and knowledge based assumption: therefore, we called our approach SAKBOT (Statistical And Knowledge Based Object deTector) [3,4]. In fact, the background model is computed frame by frame by using a statistical function (temporal median) to use, for each pixel, the most probable RGB value, but also by taking into account the knowledge acquired on the scene in the previous frames.…”

Section: Moving Object Detection and Tracking With Sakbotmentioning

confidence: 99%

Computer vision techniques for PDA accessibility of in-house video surveillance

Cucchiara

Grana

Prati

et al. 2003

First ACM SIGMM International Workshop on Video Surveillance - IWVS '03

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In this paper we propose an approach to indoor environment surveillance and, in particular, to people behaviour control in home automation context. The reference application is a silent and automatic control of the behaviour of people living alone in the house and specially conceived for people with limited autonomy (e.g., elders or disabled people). The aim is to detect dangerous events (such as a person falling down) and to react to these events by establishing a remote connection with low-performance clients, such as PDA (Personal Digital Assistant). To this aim, we propose an integrated server architecture, typically connected in intranet with network cameras, able to segment and track objects of interest; in the case of objects classified as people, the system must also evaluate the people posture and infer possible dangerous situations. Finally, the system is equipped with a specifically designed transcoding server to adapt the video content to PDA requirements (display area and bandwidth) and to the user's requests. The main issues of the proposal are a reliable real-time object detector and tracking module, a simple but effective posture classifier improved by a supervised learning phase, and an high performance transcoding inspired on MPEG-4 object-level standard, tailored to PDA. Results on different video sequences and performance analysis are discussed.

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“…Background modelling is an important and fundamental part for many computer vision applications such as real-time tracking [2,20,21,25,26], video/traffic surveillance [9,10] and human-machine interface [23,29]. After the background is modelled, one commonly performs "background subtraction" to differentiate foreground objects (those parts are of interest to track or recognize) from the background pixels.…”

Section: Introductionmentioning

confidence: 99%

A consensus-based method for tracking: Modelling background scenario and foreground appearance

Wang

Suter

2007

Pattern Recognition

154

100

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Modelling of the background ("uninteresting parts of the scene"), and of the foreground, play important roles in the tasks of visual detection and tracking of objects. This paper presents an effective and adaptive background modelling method for detecting foreground objects in both static and dynamic scenes. The proposed method computes SAmple CONsensus (SACON) of the background samples and estimates a statistical model of the background, per pixel. SACON exploits both color and motion information to detect foreground objects. SACON can deal with complex background scenarios including non-stationary scenes (such as moving trees, rain, and fountains), moved/inserted background objects, slowly moving foreground objects, illumination changes etc.However, it is one thing to detect objects that are not likely to be part of the background; it is another task to track those objects. Sample consensus is again utilized to model the appearance of foreground objects to facilitate tracking. This appearance model is employed to segment and track people through occlusions. Experimental results from several video sequences validate the effectiveness of the proposed method.

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Detecting moving objects, ghosts, and shadows in video streams

Cited by 1,240 publications

References 21 publications

Figure-Ground Segmentation—Pixel-Based

Figure-Ground Segmentation—Pixel-Based

Computer vision techniques for PDA accessibility of in-house video surveillance

A consensus-based method for tracking: Modelling background scenario and foreground appearance

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