## 6 Video Saliency
### Datasets for Video Saliency
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline
**Dataset** & **Method** & **[PERSON] 2019** & **[PERSON] 2019** & **[PERSON] 2019** & **[PERSON] 2019-III** & **[PERSON] 2018** & **[PERSON] 2018** & **[PERSON] 2018** & **[PERSON] 2017-II** & **[PERSON] 2017** & **[PERSON] 2019** & **[PERSON] 2018** \\  & [49] & [127] & [128] & [129] & [125] & [130] & [121] & [126] & [131] & [65] & [66] \\ \hline DAVSOD [49] & ✓ & & & & & & & & & & \\ FRMS [122] & ✓(0) & ✓ & ✓ (59) & & & & & & ✓(59) & ✓ (26) & & \\ VMSI [139] & ✓ & ✓ & & & ✓ & & & & & & & \\ MCL [140] & ✓ & & & & & & & & & & & \\ SegTrack v1 [23] & ✓(13) & & & & & ✓(14) & ✓(14) & ✓(14) & ✓(10) & & & \\ UVSD [41] & ✓ & & & & & & & & & & & \\ VOS [49] & ✓ & & ✓ & & & & & & & & \\ DAVS [124] & & & ✓ & ✓ & ✓ & ✓ & ✓ & & & & & \\ IEM [42] & & & & & & & & ✓ & & & & \\ Wallflower [143] & & & & & & & & ✓ & & & & \\ MOVOS [117] & & & & & & & & ✓ & & & & \\ DG [144] & & & & & & & & ✓ & & & & \\ \hline UCFSports [134,145] & & & & & & & & & & & & ✓ \\ \hline DIFFK [135] & & & & & & & & & & & ✓ & \\ Hollywood-2 [134,146] & & & & & & & & & & & ✓ & ✓ \\ DIEM [147] & & & & & & & & & & & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Dataset/method matrix for video saliency estimation. The number in parentheses identifies the version of the dataset through the corresponding cardinality of video sequences. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & \multicolumn{3}{c}{**Stimuli Characteristics**} & \multicolumn{2}{c}{**Devices**} & \multicolumn{2}{c}{**Users**} \\ \hline
**Dataset** & \begin{tabular}{c} **Resolution** \\ **(pixels)** \\ \end{tabular} & **FPS** & **Display** & **Eye Tracker** & \
\begin{tabular}{c} **Users** \\ **(Fixations)** \\ \end{tabular} &
\begin{tabular}{c} **Users** \\ **(Objects)** \\ \end{tabular} \\ \hline DAVSOD [49] & 640 \(\times\) 360 & 30 & N/A & SMI RED 250 & 17 (1/video) & 20 \\ FBMS [122] & 288 \(\pm\) 960 & 30 & N/A & (none) & (none) & N/A \\ SegTrack XV [213] & \(<\)640 \(\times\) 360 & N/A & N/A & (none) & (none) & N/A \\ VOS [43] & 800 \(\times\) 48 & 30 & 1880 \(\times\) 1050 & SMI RED 500 & 23 & 4 \\ DAVIS [124] & 1920 \(\times\) 1080 & 24 & N/A & (none) & (none) & N/A \\ UCFSports [134] & \(<\)720 \(\times\) 480 & 10 & 22\({}^{\circ}\) 1280 \(\times\) 1024 & SMI View X & 16 & (none) \\ DHFIK [135] & 640 \(\times\) 360 & 30 & 19\({}^{\prime\prime}\) 1440 \(\times\) 900 & SMI RED 250 & 17 (1/video) & (none) \\ \hline \hline  & \multicolumn{3}{c}{**Cardinalities**} & \multicolumn{2}{c}{**Responses**} \\ \hline
**Dataset** & \begin{tabular}{c} **Total** \\ **Videos** \\ \end{tabular} & **Total Frames** & \begin{tabular}{c} **Frames** \\ **Per Video** \\ \end{tabular} & \
\begin{tabular}{c} **SPF/X/FM/OR** \\ \end{tabular} &
\begin{tabular}{c} **Project** \\ **Page** \\ \end{tabular} \\ \hline DAVSOD [49] & 187 & 23,938 & \(\sim\)128 & FX, FM, OR & [148] \\ FBMS [122] & 59 & 13,860 (720 annotated) & \(\sim\)235 (\(\sim\)12 annotated) & OR & [149] \\ SegTrack XV [213] & 14 & 1066 & \(\sim\)76 & OR & [150] \\ VOS [43] & 200 & 114,421 (7467 annotated) & \(\sim\)722 (\(\sim\)37 annotated) & OR & [151] \\ DAVIS [124] & 50 & 3455 & \(<\)69 & OR & [152] \\ UCFSports [134] & 150 & 9578 & \(\sim\)64 & SP & [153] \\ DHFFIK [135] & 1000 & 582,605 & \(\sim\)583 & FX, FM & [154] \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Selected datasets for video saliency estimation, with related features (SP = Scanpaths, FX = Fixations only, FM = Fixation maps, OR = Object Regions). DAVIS [124] (Densely Annotated VIdeo Segmentation) comprises high-resolution short sequences that are manually annotated for pixel-accurate segmentation. Each clip depicts up to two spatially-connected objects, aiming at constraining the problem to a controlled and limited domain. Finally, all sequences are labeled with multiple attributes covering challenging aspects such as clutter, blur, appearance change, and many others. UCFsports [134] was built upon the pre-existing large scale video dataset of the same name by [PERSON] et al.