# A Background Subtraction Algorithm in Complex Environments Based on Category Entropy Analysis
## 5 Conclusions  
In recent studies on background subtraction, most initial backgrounds were extracted under stable background conditions. Few studies have been conducted on the initial background extraction issue encountered in harsh background conditions such as background masking, dynamic backgrounds, and color disturbance. In this study, an initial background extraction algorithm referred to as EBBE--based on category entropy--was proposed for poor initial-background conditions. This algorithm can adapt to color disturbance due to poor quality cameras or a very low frame rate by establishing joint categories during the initial-background extraction process in order to prevent the background being mistaken for the foreground owing to excessive color disturbance. By using the joint category entropy to dynamically establish representative background categories, the algorithm can overcome the background extraction problem due to camera shake and dynamic background conditions. Compared with established algorithms, the EBBE algorithm reduces the generation of false foregrounds in circumstances where there are large color disturbances due to complex conditions like camera shake, shaking trees, and snowflakes, or due to a very low frame rate. Even in cases of background masking due to moving objects in the initial images, the method was able to find the true background categories through joint category entropy analysis, thus achieving good moving object detection results.  
Conceptualization, S.-Y.C. and C.-C.C.; Data curation, S.-Y.C.; Formal analysis, S.-Y.C.; Funding acquisition, C.-C.C. and S.S.-D.X.; Investigation, S.-Y.C.; Methodology, S.-Y.C.; Project administration, C.-C.C. and S.S.-D.X.; Resources, C.-C.C. and S.S.-D.X.; Software, S.-Y.C.; Supervision, C.-C.C.; Validation, S.-Y.C.; Writing--original draft, S.-Y.C.; Writing--review & editing, C.-C.C. and S.S.-D.X.  
This research received no external funding.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Method** & **R** & **PCC** & **Re** & **Sp** & **FPR** & **FNR** & **PWC** & **Pr** & **F-Measure** \\ \hline EBBE & 1.750 & 0.968 & 0.978 & 0.769 & 0.231 & 0.022 & 3.151 & 0.989 & 0.983 \\ codebook & 4.750 & 0.908 & 0.977 & 0.336 & 0.664 & 0.023 & 9.209 & 0.925 & 0.949 \\ GMM & 2.000 & 0.949 & 0.980 & 0.536 & 0.464 & 0.020 & 5.137 & 0.966 & 0.972 \\ KDE & 2.625 & 0.915 & 0.980 & 0.399 & 0.601 & 0.020 & 8.506 & 0.930 & 0.953 \\ PBBE & 5.250 & 0.895 & 0.979 & 0.268 & 0.732 & 0.021 & 10.455 & 0.909 & 0.942 \\ SOBS & 7.000 & 0.823 & 0.965 & 0.132 & 0.868 & 0.035 & 17.711 & 0.843 & 0.900 \\ ViBe & 4.625 & 0.911 & 0.969 & 0.290 & 0.710 & 0.031 & 8.899 & 0.936 & 0.952 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Overall results across all test sequences.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Sequence** & **Resolution** & **EBBE** & **Codebook** & **GMM** & **KDE** & **PBBE** & **SOBS** & **ViBe** \\ \hline Carnegie Mellon & 360 \(\times\) 240 & 76.3 & 210.5 & 94.2 & 114.9 & 96.0 & 87.9 & 364.1 \\ highway & 320 \(\times\) 240 & 62.0 & 145.5 & 88.3 & 131.0 & 82.0 & 116.4 & 334.1 \\ skating & 540 \(\times\) 360 & 49.4 & 90.7 & 41.7 & 84.1 & 61.8 & 58.1 & 169.9 \\ tramCrossroad\_1 fps & 640 \(\times\) 350 & 52.9 & 64.1 & 35.8 & 56.4 & 50.6 & 46.1 & 213.0 \\ badminton & 720 \(\times\) 480 & 34.3 & 54.2 & 21.8 & 41.8 & 34.8 & 31.6 & 119.7 \\ sofa & 320 \(\times\) 240 & 99.0 & 242.7 & 80.6 & 108.7 & 110.8 & 85.9 & 361.5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of processing speeds expressed in frames per second.