Image analysis for video surveillance based on spatial regularization of a statistical model-based change detection

Ziliani, F.; Cavallaro, Andrea

doi:10.1109/iciap.1999.797749

Cited by 19 publications

(8 citation statements)

References 7 publications

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“…Most of our test sequences have been processed by a background subtraction program developed at Visiowave [ZC99], which generates nicely smoothed blobs (see Fig. 5, 7, for example).…”

Section: B Background Subtractionmentioning

confidence: 99%

Multicamera People Tracking with a Probabilistic Occupancy Map

Fleuret

Berclaz

Lengagne³

et al. 2008

IEEE Trans. Pattern Anal. Mach. Intell.

727

618

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Given two to four synchronized video streams taken at eye level and from different angles, we show that we can effectively combine a generative model with dynamic programming to accurately follow up to six individuals across thousands of frames in spite of significant occlusions and lighting changes.In addition, we also derive metrically accurate trajectories for each one of them.Our contribution is twofold. First, we demonstrate that our generative model can effectively handle occlusions in each time frame independently, even when the only data available comes from the output of a simple background subtraction algorithm and when the number of individuals is unknown a priori.Second, we show that multi-person tracking can be reliably achieved by processing individual trajectories separately over long sequences, provided that a reasonable heuristic is used to rank these individuals and avoid confusing them with one another.

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“…Most of our test sequences have been processed by a background subtraction program developed at Visiowave [ZC99], which generates nicely smoothed blobs (see Fig. 5, 7, for example).…”

Section: B Background Subtractionmentioning

confidence: 99%

Multicamera People Tracking with a Probabilistic Occupancy Map

Fleuret

Berclaz

Lengagne³

et al. 2008

IEEE Trans. Pattern Anal. Mach. Intell.

727

618

View full text Add to dashboard Cite

show abstract

“…Among them, methods based on the difference picture ͑DP͒ [1][2][3][4][5][6][7][8][9][10][11][12][13][14] have been extensively developed because they satisfy almost all requirements for object detector, viz., robustness to noise, adaptability to illumination changes, and fast detection. Background subtraction, [1][2][3][4][5][6][7][8][9] which uses DP between the current and the background image, is the most popular such method. We proposed a unified framework for background subtraction, 13 which is made up of three criteria.…”

Section: Object Detection Using the Bmpmentioning

confidence: 99%

“…So there have been many researches to solve problems in detecting objects in image sequences. [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] In general object detector should be robust to some noise and has to be adaptive to illumination changes in real environments. It is also very important to detect objects as fast as possible in realtime applications.…”

Section: Introductionmentioning

confidence: 99%

Object detection and video coding using background mosaic plane

Cho

Kim

2007

Opt. Eng

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Abstract. Object detection in image sequences has a very important role in many applications, such as surveillance systems, tracking and recognition systems, and coding systems. We are interested in background subtraction, which is very popular algorithm for object detection in image sequences, and also concerned to use the result of detection in selective video coding. Especially when the camera moves and zooms in on something to track the target under drastic illumination change, it is very hard to detect the object properly, and the coding efficiency is reduced. So we generate a multiple background mosaic system, called the background mosaic plane, and use it for object detection and video coding. Some experimental results for both object detection and video coding in various environments show that the average performance of the proposed algorithm is good.

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“…Moreover if the contrast of moving objects is not sufficiently high compared to the camera noise, there might not exist a unique threshold able to get rid of noise and preserve, at the same time, the motion information. To overcome these problems, an approach based on a statistical decision rule [11] is adopted to identify the moving objects. This compares through a significance test the statistical behaviour of a small neighbour (chosen as a square window) of each pixel position in the difference image with a model of the noise that could affect the difference image.…”

Section: Uagent1mentioning

confidence: 99%