In this paper we present a novel approach for the detection of closed contours of foreground objects in videos. The proposed methodology begins with an initial localization of contours that is achieved via background subtraction technique that makes use of mixture of Gaussian distributions to model the background. The features that are used to realize an approximate foreground contour segmentation consist of magnitude of gradient at multiple orientations and phase congruency. In the next stage, canny edges of the incoming frames are computed at multiple scales and thresholds using the saturation and value components of HSV image. The approximate foreground contour is refined by reflecting it on the detected edges. A color ratio based noise and shadow line removal technique has been devised to remove the falsely segmented noise and strong shadow edges. Ultimately, to ensure closed contours, edge completion algorithm by anisotropic diffusion is applied. Once the contour is completed, it undergoes flood fill to define the foreground areas. Detailed experimental results on benchmark dataset showed that the proposed framework performs well in most of the different background scenarios. It effectively tackles the presence of shadows, illumination changes, some cases of dynamic background and thermal videos.

Date Published: 5 March 2014
PDF: 10 pages
Proc. SPIE 9026, Video Surveillance and Transportation Imaging Applications 2014, 902615 (5 March 2014); doi: 10.1117/12.2041311

Published in SPIE Proceedings Vol. 9026:
Video Surveillance and Transportation Imaging Applications 2014
Robert P. Loce; Eli Saber; Ned Lecky, Editor(s)