Shot boundary detection method based on a new extensive dataset and mixed features
원문 초록
Shot boundary detection in video is one of the key stages of video data processing. A new method for shot boundary detection based on several video features, such as color histograms and object boundaries, has been proposed. The developed algorithm was tested on the open BBC Planet Earth [1] and RAI [2] datasets, and the MSU CC datasets, based on videos used in the video codec comparison conducted at MSU, as well as videos from the IBM set, were also plotted. The total dataset for algorithm development and testing exceeded the known TRECVID datasets. Based on the test results, the proposed algorithm for scene change detection outperformed its counterparts with a final F-score of 0.9794.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
EMPLACE: Self-Supervised Urban Scene Change Detection
Robust Scene Change Detection Using Visual Foundation Models and Cross-Attention Mechanisms
Towards Generalizable Scene Change Detection
Zero-Shot Scene Change Detection
A Semantic Change Detection Network Based on Boundary Detection and Task Interaction for High-Resolution Remote Sensing Images
ZeroSCD: Zero-Shot Street Scene Change Detection
토론
이 논문과 연결된 포럼 글이 아직 없습니다.