## 6 Video Saliency
### Datasets for Video Saliency  
Table 10 illustrates the datasets that were involved in the experiments of each analyzed method for video saliency estimation, with the objective of highlighting the relevant benchmarks for recent developments in this field. We separate the datasets related to methods that target different types of ground truth data, highlighting how UCFSports [134] is in fact used by solutions belonging to both worlds. Regarding methods aimed at salient object regions, it can be observed that the most frequently-adopted datasets are FBMS [122] (Freiburg-Berkeley Motion Segmentation) and SegTrackV2 [123]. Despite not being very recent (both were released in the year 2013), they are described in-depth in the following, due to their high relevance. Conversely, datasets that are particularly old, and which have been tested against only by one or a few methods, are no further analyzed. Table 11 therefore presents detailed information for the selected datasets, reporting information on both the stimuli and the user responses. As indicated, some saliency datasets that are specific for the video domain are exclusively annotated with salient object regions [43; 122; 124; 123]. Others are collected with an eye tracking device, thus producing saliency maps based on user fixations [134; 135]. Finally, the very recent DAVSOD [49] provides both types of annotation, thus highlighting the existing relationship between these different representations. DAVSOD [49] (Densely Annotated Video Salient Object Detection) is built upon the stimuli from the DHF1K [135] (Dynamic Human Fixation 1000) eye tracking dataset, manually trimmed into short video clips. The scenes are enriched with additional annotations, which include: timestamp of the shift in visual attention, category labeling into 7 classes and 70 sub-classes, and conversion of the fixation records into hand-drawn object segmentation masks, performed per-frame by multiple annotators. FBMS [122] (Freiburg-Berkeley Motion Segmentation) is a dataset composed from existing sources ([PERSON] et al. [136] and the [PERSON] 155 [137]) as well as new sequences, for a total of 59 video clips. The videos have been specifically collected aiming at high variation in image resolution and motion types, and have been manually annotated every 20 th frame, thus providing a sparse ground truth. SegTrack [138] and SegTrackV2 [123] are among the most tested-against datasets for video saliency estimation, despite being originally addressed at video segmentation. Both versions were collected with particular attention at equally representing challenging aspects, namely: color overlap between foreground and background, inter-frame motion, and changing target shape. The second version of the dataset introduces additional sequences and annotations. VOS [43] (Video Object Segmentation) is composed of videos collected from internet sources as well as personal collections, divided into an easy and a difficult subset. One keyframe every 15 frames has been segmented at the object-level by a pool of four subjects. A different set of subjects have been asked to free-view the videos, in order to collect their eye tracking data, which are eventually used to unambiguously define and annotate the salient objects.