Camera shot boundary detection
7 papers with code • 4 benchmarks • 4 datasets
The objective of camera shot boundary detection is to find the transitions between the camera shots in a video and classify the type of camera transition. This task is introduced in SoccerNet-v2, where 3 types of transitions are considered (abrupt, logo, smooth).
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Shot boundary detection (SBD) is an important component of many video analysis tasks, such as action recognition, video indexing, summarization and editing.
Large-scale, Fast and Accurate Shot Boundary Detection through Spatio-temporal Convolutional Neural Networks
Since current datasets are not large enough to train an accurate SBD CNN, we present a new dataset containing more than 3. 5 million frames of sharp and gradual transitions.
In order to train a high-performance shot transition detector, we contribute a new database ClipShots, which contains 128636 cut transitions and 38120 gradual transitions from 4039 online videos.
Although automatic shot transition detection approaches are already investigated for more than two decades, an effective universal human-level model was not proposed yet.
In this work, we propose SoccerNet-v2, 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.
Many researches have been done on shot boundary detection, but the performance of shot boundary detection approaches is yet to be addressed for the videos having sudden illumination and object/camera motion effects efficiently.