Adaptive background mixture models for real-time tracking

Stauffer, Chris; Grimson, W. Eric L.

doi:10.1109/cvpr.1999.784637

Cited by 5,141 publications

(4,252 citation statements)

References 3 publications

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“…Many efforts have been made to improve the performance of subtracting the unmoving background, e.g. [1,2,3,4]. The traditional pixel level methods like [1,4] model the background as a set of independent pixel processes, which lose the spatial context information and often end up with noisy detection.…”

Section: Introductionmentioning

confidence: 99%

“…[1,2,3,4]. The traditional pixel level methods like [1,4] model the background as a set of independent pixel processes, which lose the spatial context information and often end up with noisy detection. Therefore many methods are proposed to utilize the spatial information between pixels [2,3], or to utilize temporal information [5] to better model the background in a scene, or combine both methods [6].…”

Section: Introductionmentioning

confidence: 99%

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Joint optimization of background subtraction and object detection for night surveillance

Lin

et al. 2011

2011 18th IEEE International Conference on Image Processing

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Detecting foreground objects for night surveillance videos remains a challenging problem in scene understanding. Though many efforts have been made for robust background subtraction and robust object detection respectively, the complex illumination condition in night scenes makes it hard to solve each of these tasks individually. In practice, we see these two tasks are coupled and can be combined to help each other. In this work, we apply a recently proposed algorithm -Feedback Enabled Cascaded Classification Models (FECCM) -to combine the background subtraction task and the object detection task into a generic framework. The proposed framework treats each classifier for the respective task as a 'blackbox', thus allows the usage of most existing algorithms as one of the classifiers. Experiment results show that the proposed method outperforms a state-of-the-art background subtraction method and a state-of-the-art object detection method.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Joint optimization of background subtraction and object detection for night surveillance

Lin

et al. 2011

2011 18th IEEE International Conference on Image Processing

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“…GMM GMM proposed in [2] describe a background pixel using a mixture of K Gaussian distributions. So, it can deal with more complex background scenes, such as flapping flags and waving branches.…”

Section: Related Workmentioning

confidence: 99%

“…Among them are running Gaussian average [1], Gaussian mixture model (GMM) [2], kernel density estimation [3], and eigenbackground [4]. An admirable review of these techniques is presented in [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Multi-Target Tracking Using Color Information

Kim¹,

Khan²,

Awan³

et al. 2014

IJCCE

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Abstract-For security purposes, it is prerequisite to track multiple targets efficiently. Most of the current implementation uses Kalman filter and color information independently. The proposed method combines extended Kalman filter and color information for tracking multiple objects under high occlusion. For tracking, the first thing done is the object detection. The background model used to segment foreground from background is spatio-temporal Gaussian mixture model (STGMM). Tracking consists of two steps: independent object tracking and occluded object tracking. For independent object tracking we exploit extended Kalman filter, whereas for occluded object tracking, color information attribute is used. The system was tested in real world application and successful results were obtained.

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“…Much work has been done since the introduction of the Mixture of Gaussian (MOG) model [3] by Stauffer and Grimson [4]. In their approach, the mixture of K Gaussian representing the statistics of one pixel over the time can cope with multi-modal background distribution.…”

Section: Introductionmentioning

confidence: 99%

Subsequent Processing of Background Modeling for Traffic Surveillance System

Tran¹,

Nguyên²,

Nhu³

et al. 2016

IJCTE

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Abstract-The background model finds difficult to extract the good foreground if it is implemented in real-time, the change of light and weather that make the detected-foreground unclear, has countless noise. Therefore, to avoid that problem, the Mixture of Gaussian need subsequent processing step to make the foreground better. The paper proposes a method, degrades several drawbacks of Background Subtraction, which intended to be used in urban traffic scenes.

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Adaptive background mixture models for real-time tracking

Cited by 5,141 publications

References 3 publications

Joint optimization of background subtraction and object detection for night surveillance

Joint optimization of background subtraction and object detection for night surveillance

Multi-Target Tracking Using Color Information

Subsequent Processing of Background Modeling for Traffic Surveillance System

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