Research Article
Moving Object Detection Algorithm Using Gaussian Mixture Model and SIFT Keypoint Match
@INPROCEEDINGS{10.1007/978-3-319-73564-1_3, author={Hang Dong and Xin Zhang}, title={Moving Object Detection Algorithm Using Gaussian Mixture Model and SIFT Keypoint Match}, proceedings={Machine Learning and Intelligent Communications. Second International Conference, MLICOM 2017, Weihai, China, August 5-6, 2017, Proceedings, Part I}, proceedings_a={MLICOM}, year={2018}, month={2}, keywords={Moving object detection GMM SIFT keypoint match}, doi={10.1007/978-3-319-73564-1_3} }- Hang Dong
Xin Zhang
Year: 2018
Moving Object Detection Algorithm Using Gaussian Mixture Model and SIFT Keypoint Match
MLICOM
Springer
DOI: 10.1007/978-3-319-73564-1_3
Abstract
In the field of image processing, Gaussian mixture model (GMM) is always used to detect and recognize moving objects. Due to the defects of GMM, there are some error detections in the final consequence. In order to eliminate the defects of GMM in moving objects detections, this paper has studied a moving object detection algorithm, combining GMM with scale-invariant feature transform (SIFT) keypoint match. First, GMM is built to obtain the distributions of background image pixels. Then, morphological processing method is applied to improve the quality of binary segmentation image and extract segmentation images of moving objects. Finally, SIFT keypoint match algorithm is used to eliminate misjudgment segmentation images by judging whether the segmentation image matches with the background template or not. Compared with original GMM, the results show that the accuracy of moving object detection has been improved.
