Occlusion Boundary Detection by Combining Appearance, Motion and Edge Structure Cues in Video
ZHANG Shi-hui1,2,ZHANG Hong-qiao1,HAN De-wei1
1. School of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei 066004,China
2. The Key Lab for Computer Virtual Tech & Sys Integration of Hebei Province, Qinhuangdao, Hebei 066004,China
Abstract：To detect the occlusion boundary in video sequences accurately, an occlusion boundary detection approach based on random forests classifier is proposed. Firstly, the edges of current frame in a video are segmented to obtain superpixels and superpixel edges, and then the superpixels edges are decomposed into short line fragments. Secondly, the occlusion related features of each line fragment are extracted by combining appearance, motion and edge structure cues and the extracted features are assembled to feature vector. After that, the feature vector of each line fragment is inputted to the occlusion boundary classifier to detect whether each line fragment is an occlusion boundary or not. Finally, the occlusion boundary of the current frame in a video is obtained by visualizing all the line fragments which belong to occlusion boundary. The experimental results show that the proposed approach has higher accuracy.
［1］Toh P S, Forrest A K. Occlusion detection in early vision［C］// Proceedings of 3rd International Conference on Computer Vision, Piscataway, USA,1990, 126-132.
［2］孔明,王式民.共轴法立体视觉三维测量的研究［J］.计量学报,2004,25(4):294-297.
［3］郭大波,卢朝阳,焦卫东,等.立体图像的遮挡边界区域检测技术［J］.中国体视学与图像分析, 2008,13(1):17-20.
［4］Ince S, Konrad J. Geometry-based estimation of occlusions from video frame pairs［C］// Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Piscataway, USA, 2005,II 933-936.
［5］Stein A N, Hebert M. Local detection of occlusion boundaries in video［J］. Image and Vision Computing, 2009,27(5):514-522.
［6］Stein A N, Hebert M. Occlusion boundaries from motion: Low-level detection and mid-level reasoning［J］. International Journal of Computer Vision,2009,82(3): 325-357.
［7］Sargin M E, Bertelli L, Manjunath B S, et al. Probabilistic occlusion boundary detection on spatio-temporal lattices［C］// Proceedings of the IEEE International Conference on Computer Vision, Piscataway, USA,2009,560-657.
［8］He X, Yuille A. Occlusion boundary detection using pseudo-depth［C］// Proceedings of the European Conference on Computer Vision, Heidelberg, Germany: Springer Verlag, 2010,539-552.
［9］Sundberg P, Brox T, Maire M, et al. Occlusion boundary detection and figure/ground assignment from optical flow［C］// Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Piscataway, USA,2011,2233-2240.
［10］Jacobson N, Freund Y, Nguyen T Q. An online learning approach to occlusion boundary detection［J］. IEEE Transactions on Image Processing,2012,21(1):252-261.
［11］Chen D, Yuan Z, Zhang G, et al. Detecting occlusion boundaries via saliency network［C］// Proceedings of International Conference on Pattern Recognition, Piscataway, USA, 2012,2569-2572.
［12］Maire M, Arbeláez P, Fowlkes C, et al. Using contours to detect and localize junctions in natural images［C］// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,Piscataway,USA,2008,1-8.
［13］Sun D, Roth S, Black M J. Secrets of optical flow estimation and their principles［C］// Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Piscataway, USA, 2010, 2432-2439.
［14］Arbelaez P, Maire M, Fowlkes C, et al. From contours to regions: an empirical evaluation［C］// Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Piscataway, USA, 2009, 2294-2301.
2016, Vol. 37 
