Camera shot boundary detection

A novel large-scale corpus of manual annotations for the SoccerNet video dataset, along with open challenges to encourage more research in soccer understanding and broadcast production. Source: SoccerNet-v2 : A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos

ClipShots is a large-scale dataset for shot boundary detection collected from Youtube and Weibo covering more than 20 categories, including sports, TV shows, animals, etc. In contrast to previous shot boundary detection datasets, e.g. TRECVID and RAI, which only consist of documentaries or talk shows where the frames are relatively static, ClipShots contains moslty short videos from Youtube and Weibo. Many short videos are home-made, with more challenges, e.g. hand-held vibrations and large occlusion. The types of these videos are various, including movie spotlights, competition highlights, family videos recorded by mobile phones etc. Each video has a length of 1-20 minutes. The gradual transitions in the dataset include dissolve, fade in fade out, and sliding in sliding out. Source: https://github.com/Tangshitao/ClipShots

TRECVID is a yearly set of competitions centered on video retrieval and indexing, hosting a variety of video data sets. Source: YouTube-BoundingBoxes: A Large High-PrecisionHuman-Annotated Data Set for Object Detection in Video Image Source: https://www-nlpir.nist.gov/projects/tv2016/tv2016.html

This is a dataset for a shot boundary detection task. The dataset contains 2 existing datasets and 19 manually marked up open source videos with a total length of more than 1200 minutes and 10000 scene transitions. The dataset includes different types of videos with different resolutions from 360×288 to 1920×1080 in MP4 and MKV formats. Videos include samples in RGB scale or in grayscale with FPS from 23 to 60.