动态背景下基于自更新像素共现的前景分割

梁栋,刘昕宇,潘家兴,孙涵,周文俊,金子俊一

Foreground segmentation under dynamic background based on self-updating co-occurrence pixel

Dong LIANG,Xin-yu LIU,Jia-xing PAN,Han SUN,Wen-jun ZHOU,Shun’ichi KANEKO

表 2 不同方法的CDNet2014数据集F-measure对比

Tab.2 F-measure of different methods on CDNet2014

序号 算法 F-measure BDW BSL CJT DBG IOM SHD THM TBL LFR NVD PTZ 1 SU-CPB 0.867 0.907 0.853 0.924 0.760 0.910 0.969 0.895 0.449 0.558 0.753 2 CPB[17] 0.475 0.519 0.597 0.477 0.348 0.581 0.372 0.459 0.170 0.277 0.161 3 SuBSENSE[6] 0.862 0.950 0.815 0.818 0.657 0.865 0.817 0.779 0.645 0.560 0.348 4 KDE[3] 0.757 0.909 0.572 0.596 0.409 0.803 0.742 0.448 0.548 0.437 0.037 5 GMM[2] 0.738 0.825 0.597 0.633 0.521 0.732 0.662 0.466 0.537 0.410 0.152 6 BMOG[8] 0.784 0.830 0.749 0.793 0.529 0.840 0.635 0.693 0.610 0.498 0.235 7 SGSM-BS[11] 0.856 0.950 0.820 0.848 0.819 0.890 0.850 0.850 0.750 0.510 − 8 STAM[22] 0.970 0.989 0.899 0.948 0.916 0.966 0.991 0.933 0.668 0.710 0.865 9 DeepBS[9] 0.830 0.958 0.899 0.876 0.610 0.930 0.758 0.846 0.600 0.584 0.313 10 CascadeCNN[12] 0.943 0.979 0.976 0.966 0.851 0.941 0.896 0.911 0.837 0.897 0.917 11 DPDL[13] 0.869 0.969 0.866 0.869 0.876 0.936 0.838 0.764 0.708 0.611 0.609 12 FgSegNet[14] 0.984 0.998 0.995 0.994 0.993 0.995 0.992 0.978 0.956 0.978 0.989