## 4 Evaluation
We compared RoiSeg with state-of-the-art unsupervised learning methods on FBMS dataset, as shown in Figure 15 and Table 7. It is observed that the image saliency methods rendering of information within frames can produce unsatisfactory results, and even some images miss foreground objects, mainly because the time correlation in the image sequence to convey the target information was not taken into consideration [40]. However, these foreground segmentation methods based on motion perform better than the image saliency methods [41; 42; 43]. RoiSeg estimated the target object in a more cluttered background with higher real-time boundary and splits video objects in a completely unsupervised manner. We also conducted experiments to compare the performance of RoiSeg and other segmentation methods on a different dateset (SegTrack), as shown in Tables 8 and 9. Figure 13: Experimental results on eight sequences. Left to right: original images, DPWrenGABGS, MixtureOfGaussianV1 BGS, MultiLayerBGS, PixelBasedAdaptiveSegmenter, LBAdaptiveSOM, proposed method. Top to bottom: “112”, “122”, “212”, “222”, “312”, “322”, “412”, “422”. In Table 8, References [41; 42; 43; 45; 47; 48; 49; 50] are unsupervised learning methods, while [39; 51] are the supervised learning methods. The results demonstrated that RoiSeg could meet the requirements of most tasks, although its performance was not as good as state-of-the-art segmentation methods. In Table 9, References [41; 44; 45; 48; 52] are unsupervised learning methods, while [46; 50; 51] are supervised learning methods. Among them, ref [46] utilized the CNN methods and VOC 2011 [53] for pre-training and testing, respectively. Table 9 shows that the result of IoU evaluation on RoiSeg was similar to that of APFPER and the CNN-based approaches had an absolute advantage in supervised segmentation tasks, but it relied on too much data. We also conducted extensive experiments to evaluate the real-time performance as shown in Table 10 and RoiSeg achieved a better performance in terms of real-time operations. In summary, RoiSeg outputted the expected results on some video sequences compared to the best performing unsupervised learning methods. There is a \begin{table}
\begin{tabular}{c c c c c c} \hline
**Video** & **RoiSeg** & **[42]** & **[44]** & **[45]** & **[41]** & **[46]** \\ \hline Bear2 & 63.51 & 87.52 & 21.14 & 86.81 & 70.11 & 88.92 \\ \hline Cars5 & 15.62 & 10.71 & 38.73 & 17.38 & 38.52 & 60.11 \\ \hline Cars9 & 30.17 & 19.55 & 28.92 & 52.44 & 60.08 & 77.82 \\ \hline Cats1 & 78.83 & 19.75 & 81.49 & 83.11 & 85.72 & 70.13 \\ \hline People1 & 58.63 & 56.06 & 64.82 & 53.33 & 68.12 & 77.07 \\ \hline People5 & 55.82 & 10.71 & 84.43 & 51.81 & 56.41 & 73.31 \\ \hline Rabbits2 & 56.01 & 20.41 & 47.81 & 28.32 & 71.06 & 79.12 \\ \hline Avg. & 51.23 & 32.10 & 52.48 & 53.31 & 64.29 & 75.21 \\ \hline \end{tabular}
\end{table} 
Table 7: Comparison based on IoU between RoiSeg and other methods on FBMS dataset. Figure 14: Experimental results on four sequences. Left to right: original images, DPWrenGBGS, MixtureOfGaussianV1 BGS, MultiLayerBGS, PixelBasedAdaptiveSegmenter, LBAdaptiveSOM, proposed method. Top to bottom: “512”, “522”, “My_video1”, “My_video2”. Figure 15: Comparison between RoiSeg and state-of-the-art unsupervised learning methods on FBMS.