# Personalized Video Summarization: A Comprehensive Survey of Methods and Datasets
## 6 Personalized Video Datasets
It contains 52 videos, one to nine minutes long, that have been recorded in various ways such as egocentric, dynamic, and static views. Each video contains 25 summaries of people. All videos are minimally edited [61]. 13. _UT Egocentric (UTE):_ It was published in 2012. Using the portable Looxcie camera, 10 videos have been recorded with a resolution of 320 \(\times\) 480 pixels and 15 fps each. Each video has a duration of 3 to 5 h, and the total duration of all videos is 37 h. Videos capture various physical daily activities. It is based on the most important people and the most important objects. It is used to produce first-person view-based video summaries. Object annotation is included in the video data [82]. 14. _SumMe:_ This dataset was created in 2014. It includes 25 videos that are slightly processed or raw. The content of the videos is related to sports, events, and holidays. The duration of each video varies from 1 to 6 min. The dataset was annotated for video segments with human scores [80]. 15. _TVSum:_ It was published in 2015. This dataset includes a collection of 50 YouTube videos. Videos are divided into 10 categories. Each category contains 5 videos, and each video represents a specific genre such as documentaries, news, and egocentrics. The duration of each video is 2 to 10 min. Through crowd sourcing, video scores are annotated at the level of shots [81]. 16. _YouTube Action:_ This dataset [91] was published in 2009. This dataset includes, from 11 categories of realistic action, 1581 videos [15]. 17. _UTEgo:_ It was created in 2012. Using the Looxcie Wearable Camera, the 10 videos were collected. Each video has a resolution of 320 \(\times\) 480 pixels at 15 fps that lasts 3 to 5 h. Various activities are recorded in the videos, such as cooking, driving, lecture tracking, shopping, and food. As ground truth, the annotations taken in Muruk were used. Each video has annotated frames that average 680 [82]. * _CSumm:_ This dataset was published in 2017. Using a Google glass, 10 videos were captured with a 720 \(\times\) 1280 pixel resolution at 29 fps. Each video is annotated, lasts between 15 and 30 min, and is non-restrictive. Some of the activities included in the videos are having dinner, enjoying nature, watching sports, exercising, etc. A wide range of motions and viewpoints are included in the videos. The best segments are manually annotated with a 10 s time limit [68]. * _Query-Focused dataset (QFVS):_ It was published in 2017. The repository includes 4 videos with different scenarios of everyday life that are uncontrollable. Each video lasts between 3 and 5 h. For each user's query, a dictionary of 48 concepts related to everyday life is provided in order to make a video summary based on queries [64]. Users annotate in each video the absence/presence of concepts [5]. * _Animation:_ From 32 cartoons, the dataset includes 148 videos. Each video includes 3000 bullet screen comments and lasts about 24 min [29]. * _Movie:_ The dataset includes 19 movies, 2179 bounding boxes, and 96 characters. The sample tests consist of 452 bounding boxes and 20 characters [29]. * _Recola:_ This dataset is used to identify emotions using multiple models [92]. The Recola dataset was used by [PERSON] and [PERSON] to evaluate and educate Cer-Net [32]. * _TV episodes:_ This dataset was published in 2014. It includes ground truth summaries and text annotations for TV episodes. The number of episodes is 4 and the duration of each episode is 45 min. The total duration of the annotations is 40 h of data. In 11 videos, the 40 h separation takes place [83]. * _Large-scale Flickr images:_ This dataset was created in 2021. It includes 420,000 athletic images that were shared by photographers. Images are divided into seven categories, and 30,000 queried photos are taken in response to each category. Pictures of \"Interest\" are the photos that appear after each question. To train the improved deep model, images of \"no interest\" and \"interest\" are used [30]. * _ActivityNet_[93]: It was published in 2015.