Affiliation:
1. Yunnan University
2. Yannan Police Officer Academy 2
Abstract
in order to solve the adverse effects of strong light and shadow on the test results, a fusion frame difference and background subtraction method in the HSV space is used in this paper. By using frame difference method to solve the effect of strong light, but frame difference method can not detect object when the object do not move, the method of background subtraction can detect it, building Gaussian background model in the HSV space can eliminate shadows. Empirical results show that the method of fusion frame difference and background subtraction in the HSV space can get overcome the effect of strong light and shadows. Fusion background subtraction and frame difference method based on establishing a Gaussian mixture model in HSV space can overcome the disadvantages of the frame difference method, at the same time it can also solve the false detection of object which result from the background subtraction method.
Publisher
Trans Tech Publications, Ltd.
Reference14 articles.
1. Dar-Shyang Lee. Effective Gaussian Mixture Learning for Video Background Subtraction [J]. IEEE TRANSAC TIONS ON PATTERN ANALYSI S AND MACHI NE INTELLIGEN CE27(5), 827-832 (2005).
2. Yanpin Zhang, YunqiuBai. background Application of Improved Gaussian mixture model moving object detection[J]. Computer Engineering and Applications, 46(34), 155-158 (2010).
3. Shao-Wen Yang and Chieh-Chih Wang. Simultaneous egomotion estimation, segmentation, and moving object detection. Journal of Field Robotics, 28(4), 565-588(2011).
4. Xuehua Song, Yu Chen. Moving object detection based on improved Gaussian mixture background model[J]. Computer Engineering and Design, 31(21), 4646-4649(2010) Fig. 2 the result of this paper.
5. Jibin Fu, Xin Bai, Baode Ju. A video object detection algorithm based on pixels texture correlation background model[C]. IEEE, Beijing China: Intelligent Visual Surveillance (IVS), Third Chinese Conference o, 2011, 61-64(2011).