Improvement of the Gaussian Mixture Model Based on EmguCV Motion Target Detection Design
- DOI
- 10.2991/meici-16.2016.49How to use a DOI?
- Keywords
- K-means; Improved frame difference; Moving targets detection; EmguCV; The Gaussian mixture model
- Abstract
Introduces a kind of video moving object detection based on mixture Gaussian model design, mainly combined with frame difference method and the Gaussian mixture model, then use K-means clustering algorithm, for complex scenarios different update rate is realized by using different background region, to complete the moving target detection, respectively, compared to traditional Gaussian mixture model (Gaussian mixture model, GMM) and the traditional frame difference detection method, in order to better solve the problem of motion target detection in complex scene, and mainly analyze based on frame difference and EmguCV framework, and finally using C# and EmguCV framework for moving targets detection. Application through the experiment show that the design is easy to use, and the performance is better. Introduces a kind of video moving object detection based on mixture Gaussian model design, mainly combined with frame difference method and the Gaussian mixture model, then use K-means clustering algorithm, for complex scenarios different update rate is realized by using different background region, to complete the moving target detection, respectively, compared to traditional Gaussian mixture model (Gaussian mixture model, GMM) and the traditional frame difference detection method, in order to better solve the problem of motion target detection in complex scene, and mainly analyze based on frame difference and EmguCV framework, and finally using C# and EmguCV framework for moving targets detection. Application through the experiment show that the design is easy to use, and the performance is better. Introduces a kind of video moving object detection based on mixture Gaussian model design, mainly combined with frame difference method and the Gaussian mixture model, then use K-means clustering algorithm, for complex scenarios different update rate is realized by using different background region, to complete the moving target detection, respectively, compared to traditional Gaussian mixture model (Gaussian mixture model, GMM) and the traditional frame difference detection method, in order to better solve the problem of motion target detection in complex scene, and mainly analyze based on frame difference and EmguCV framework, and finally using C# and EmguCV framework for moving targets detection. Application through the experiment show that the design is easy to use, and the performance is better. Introduces a kind of video moving object detection based on mixture Gaussian model design, mainly combined with frame difference method and the Gaussian mixture model, then use K-means clustering algorithm, for complex scenarios different update rate is realized by using different background region, to complete the moving target detection, respectively, compared to traditional Gaussian mixture model (Gaussian mixture model, GMM) and the traditional frame difference detection method, in order to better solve the problem of motion target detection in complex s
- Copyright
- © 2016, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Qingyu Guo AU - Zheng Zhang PY - 2016/09 DA - 2016/09 TI - Improvement of the Gaussian Mixture Model Based on EmguCV Motion Target Detection Design BT - Proceedings of the 2016 6th International Conference on Management, Education, Information and Control (MEICI 2016) PB - Atlantis Press SP - 231 EP - 235 SN - 1951-6851 UR - https://doi.org/10.2991/meici-16.2016.49 DO - 10.2991/meici-16.2016.49 ID - Guo2016/09 ER -