# Personalized Video Summarization: A Comprehensive Survey of Methods and Datasets
## 6 Personalized Video Datasets
This dataset includes 20,000 videos and 100k descriptions. The total duration of all videos is 849 h. Each video has an average duration of 180 s [78]. * _Query-video pair based:_ This dataset was published in 2020. The repository includes 190 YouTube videos with annotations for each video. All videos are sampled with one frame per second (FPS). Annotations are frame-based. Amazon Mechanical Turk (AMT) was used to produce frame annotations with the labels of the question scores that are relevant and text-based [21]. * _MV51K:_ It was published in 2019. This dataset includes 10 queries of around 1000 videos. Videos are crawled by YouTube, and web images that are relevant, annotations that are manual, and video tags. As queries have been selected from Wikipedia News, the list includes 10 hot events from 2011 to 2016. For each query, about 100 videos were collected from YouTube. Each video lasts between 0 and 4 min. Annotation was applied to videos of human judgments [71]. * _TRECVID 2001:_ This dataset was created in 2001. It includes TV and documentaries videos. Videos are short, that is, less than 2 min, or long, that is, longer than 15 min [59]. * _INRIA Holiday:_ This dataset includes mostly high-resolution personal vacation photos. A very wide variety of scene types are included in the dataset. Each individual scene is represented by one of the 500 groups of images contained in the dataset [94]. * _ImageNet:_ This dataset was published in 2009. It includes 5247 categories with 3.2 million annotated images in total [95]. * _UCF101:_ This dataset was published in 2012. It contains more than 13K clips, which are downloaded from YouTube. The videos are of type AVI, have a resolution of \(320\times 240\) and a frame rate of 25 fps. In the dataset, videos are divided into 5 types and include 101 action categories [96]. 32. _Relevance and Diversity Dataset (RAD)_: This dataset was created in 2017. It is a query-specific dataset. It includes 200 annotated videos with query-specific and diversity-relevant labels. Given a different question, the retrieval of each video was performed. The top queries were pulled between 2008 and 2016 from YouTube with seed queries in 22 different categories. Each video is between 2 to 3 min long and is sampled at one frame per second [97]. * _FineGym:_ It was published in 2020. This dataset includes 156 high-resolution videos (720p and 1080p) from a YouTube gymnasium of 10 min each. The annotations provided in FINEGYM are related to the fine-grained recognition of human action in gymnastics [98]. * _Disney:_ This dataset was published in 2012. Videos were collected by more than 25 people using a GoPro camera mounted on the head. The videos have resolutions of \(1280\times 720\) and were sampled at 30 FPS. The dataset includes 8 topics and the total duration of the videos is more than 42 h. At 15 fps, the images were extracted, and in total more than 2 million images were exported. Throughout the video, the end and start times of the intervals corresponding to the types of social interaction were manually labeled [99]. * _HUIJ:_ This dataset [100] was created in 2016. This repository contains 44 self-centered videos of daily activities, performed both externally and internally, by 3 people. Each video is less than 30 min long [25]. * _New YouTube:_ Includes 25 videos in 5 categories, such as food, urban placement, natural scene, animals, and landmark [51]. * _YouTube Highlights:_ This dataset was published in 2014. It includes YouTube videos in six different fields, such as skiing, surfing, parkour, dogs, gymnastics, and skating. Each section contains about 100 videos of varying lengths. The total duration of all videos is equal to 1430 min. The dataset has been labeled using Amazon Mechanical Turk for evaluation and analysis purposes [101]. Table 3 provides some datasets with their characteristics that were used for personalized video summarization.