Previous [ 1] [ 2] [ 3] [ 4] [ 5] [ 6] [ 7] [ 8] [ 9] [ 10] [ 11] [ 12] [ 13] [ 14] [ 15] [ 16] [ 17] [ 18] [ 19]


Journal of Information Science and Engineering, Vol. 30 No. 6, pp. 1865-1886 (November 2014)

A Statistical Method for Generic Foreground Detection*

1Department of Electrical Engineering
National Taiwan University of Science and Technology
Taipei, 106 Taiwan
2Industrial Technology Research Institute
Hsinchu, 310 Taiwan

Traditional approaches such as Gaussian mixture model (GMM), OtsuíŽs and moment preserving (MP) methods are developed for segmentation of opaque objects. For semi-opaque objects like flame and smoke the result is cluttered, due to inappropriate threshold, especially if one dominates the other. Besides, rapidly changing environments like foggy and rainy scenes increase the difficulty in foreground detection. We propose a statistical method for the detection of both opaque objects and semi-opaque objects that works in all weather conditions. We use difference of histogram and ANOVA for candidate foreground detection and StudentíŽs t-test for object segmentation. Experiments are conducted for both opaque and semi-opaque objects under both clear and severe weather conditions. The results show that for opaque objects, the recall of the proposed is 0.941, while for semi-opaque objects, the recall is 0.895. In the scenes where both types of objects exist, the recall remain at 0.901. In severe weather conditions, the recalls are 0.93 and 0.88 for opaque and semi-opaque objects, respectively.

Keywords: object detection, subtraction techniques, image segmentation, semi-opaque object, histograms, analysis of variance, student t-test

Full Text (ą■Ąň└╔) Retrieve PDF document (201411_11.pdf)

Received December 26, 2012; revised April 7 & May 29, 2013; accepted May 30, 2013.
Communicated by Jen-Hui Chuang.
* The 25th IPPR Conference on Computer Vision, Graphics and Image Processing held during August 12 and 14, 2012 at Sun-Moon Lake by IPPR, Taiwan.