Proceedings Article10.1109/ICIP.2005.1529946
Performance characterization for Gaussian mixture model based motion detection algorithms
Junwen Wu,Mohan M. Trivedi +1 more
- 14 Nov 2005
- Vol. 1, pp 1097-1100
19
TL;DR: Qualitative and quantitative performance comparisons of these two approaches are presented and the Gaussian mixture globally for modeling the distribution of the difference image between the new frame and the estimated background is proposed.
read more
Abstract: Pixelwise Gaussian mixture based background modeling algorithm proposed in S. Stauffer and W.E.L. Grimson (1999 and 2000) has been proved to be robust for many motion detection applications. However, the algorithm is not sensitive to fast motion. One possible solution is to introduce local correlations. Starting from this, this paper proposes to use the Gaussian mixture globally for modeling the distribution of the difference image between the new frame and the estimated background. Experimental evaluation validates the algorithm. Motivated by the demands of selecting the more appropriate algorithm for a specific application, qualitative and quantitative performance comparisons of these two approaches are presented. We proposes three metrics. One is to characterize the pixel level accuracy and the other two are to evaluate the errors in the object level. Pros and cons of both algorithms are summarized.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey
TL;DR: The purpose of this paper is to provide a survey and an original classification of improvements of the original MOG, and to discuss relevant issues to reduce the computation time.
An improved motion detection method for real-time surveillance
Nan Lu,Jihong Wang,Qinghua Wu,Li Yang +3 more
- 01 Jan 2008
TL;DR: The most attractive advantage of the proposed algorithm for motion detection is that the algorithm does not need to learn the background model from hundreds of images and can handle quick image variations without prior knowledge about the object size and shape.
Robust spatio-temporal multimodal background subtraction for video surveillance
TL;DR: A novel background subtraction technique derived from the popular mixture of Gaussian models technique (MGM), which discard the Gaussian assumptions and use models existing of an average and an upper and lower threshold to result in a robust object detection technique that deals with several difficult situations.
18
Motion Detection Based on Background Modeling and Performance Analysis for Outdoor Surveillance
Tianci Huang,Jingbang Qiu,Takahiro Sakayori,Satoshi Goto,Takeshi Ikenaga +4 more
- 20 Feb 2009
TL;DR: An effective scheme to improve the adaptive background model for each pixel by introducing a background training parameter into every Gaussian model, and region-based scheme is applied to judgment by utilizing both spatial and temporal information is proposed.
15
Improved background mixture models for video surveillance applications
Chris Poppe,Gaëtan Martens,Peter Lambert,Rik Van de Walle +3 more
- 18 Nov 2007
TL;DR: An update of the popular Mixture of Gaussian Models technique is proposed, showing a lack of this technique to cope with quick illumination changes and a different matching mechanism is proposed to improve the general robustness.
14
References
Adaptive background mixture models for real-time tracking
Chris Stauffer,W.E.L. Grimson +1 more
- 23 Jun 1999
TL;DR: This paper discusses modeling each pixel as a mixture of Gaussians and using an on-line approximation to update the model, resulting in a stable, real-time outdoor tracker which reliably deals with lighting changes, repetitive motions from clutter, and long-term scene changes.
Learning patterns of activity using real-time tracking
Chris Stauffer,W.E.L. Grimson +1 more
TL;DR: This paper focuses on motion tracking and shows how one can use observed motion to learn patterns of activity in a site and create a hierarchical binary-tree classification of the representations within a sequence.
W/sup 4/: real-time surveillance of people and their activities
TL;DR: W/sup 4/ employs a combination of shape analysis and tracking to locate people and their parts and to create models of people's appearance so that they can be tracked through interactions such as occlusions.
3K
Non-parametric Model for Background Subtraction
Ahmed Elgammal,David Harwood,Larry S. Davis +2 more
- 26 Jun 2000
TL;DR: A novel non-parametric background model that can handle situations where the background of the scene is cluttered and not completely static but contains small motions such as tree branches and bushes is presented.
A Bayesian computer vision system for modeling human interactions
TL;DR: A real-time computer vision and machine learning system for modeling and recognizing human behaviors in a visual surveillance task and demonstrates the ability to use these a priori models to accurately classify real human behaviors and interactions with no additional tuning or training.