# Personalized Video Summarization: A Comprehensive Survey of Methods and Datasets
## 6 Personalized Video Datasets  
Datasets are needed to test, train, and benchmark various personalized video summarization techniques. Figure 10 depicts personalized video datasets (\(x\)-axis) and the number of corresponding articles (\(y\)-axis) that report results on each of them. The following datasets have been created to evaluate the summarization of personalized videos. 1. _Virtual Surveillance Dataset for Video Summary (VSSum):_ According to the permutation and combination method, [PERSON] et al. [86] created a dataset in 2022. The dataset consists of virtual surveillance videos and includes 1000 videos that are simulated in virtual scenarios. Each video has a resolution of 1920 \(\times\) 1080 in 30 fps and a duration of 5 min. 2. _The Open Video Project (OVP):_ This dataset was created in 1999, including 50 videos recorded at a resolution of 352 \(\times\) 240 pixels at 30 fps, which are in MPEG-1 format and last from 1 to 4 min. The types of content are educational, lecture, historical, ephemeral, and documentary [87]. This repository contains videos longer than 40 h, mostly in MPEG-1 format, pulled from American agencies such as NASA and the National Archives. A wide range of video types and features are reflected in the repository content, such as home, entertainment, and news videos [84]. 3. _TVC:_ In 2019, [PERSON] et al. [53] created the TVC dataset which consists of 618 TV commercial videos. Figure 10: Personalized video datasets (\(x\)-axis) and the number of corresponding papers (\(y\)-axis) that report results on each of them (SumMe [80], TVSum [81], UT Egocentric [82], TV episodes [83], Query-Focused [64], TVC [53], OVP [84], Sports-1M [85]). 4. _Art City:_ The largest unconstrained dataset of heritage tour visits, including egocentric videos sharing narrative landmark information, behavior classification, and geolocation information, was published by [PERSON] et al. [62] in 2017. The videos have recorded cultural visits to six art cities by tourists and contain geolocation information. 5. _Self-compiled:_ Based on the UT Egocentric (UTE) dataset [82], a new dataset was compiled after collecting dense concept annotations on each video capture by [PERSON] et al. [5] in 2017. The new dataset is designed for query-focused summarization to provide different summarizations depending on the query and provide automatic and efficient evaluation metrics. 6. _Common Film Action:_ This repository includes 475 video clips from 32 Hollywood films. The video clip that contains it is from eight categories of actions that have been annotated [88]. 7. _Soccer video:_ This dataset contains 50 soccer video clips whose annotation was manually collected from the UEFA Champions League and the FIFA World Cup 2010 championship [36]. 8. _UGSV Kondo:_ This dataset was created in 2016. It contains 10 RGB-D videos containing 12 combats totaling 90 min. Videos were recorded using a Microsoft Kinect V2 sensor [89]. Eighteen additional self-recorded RGB-D Kondo videos were added to the dataset. The videos have a total duration of 246 min and a frame of around 20 fps [45]. 9. _Hollywood-2:_ It includes 2859 videos with a resolution of 400-300 \(\times\) 300-200 pixels from 69 films, some of which are educational and others test. For testing, it is 884 actions and for training, it is 823 actions. For testing, it is 582 scenes, and for training, it is 570 scenes [90]. 10. _Sports-IM:_ It was published in 2014. This dataset includes 1 million YouTube sports videos that have been annotated in 487 classes. Each class includes 1000-3000 videos, and five percent of the videos have been annotated with at least one class. On average, each video is 5 min and 36 s long [85]. 11. _[PERSON] dataset:_ This dataset includes 90 video sequences which have a resolution of 180 \(\times\) 144 pixels and are de-interlaced at 50 fps. The videos show for each of the 9 people the 10 physical actions they perform [63]. 12. _UGSum52:_ This dataset was created in 2019.