Shot Boundary Detection with Spatial-Temporal Convolutional Neural Networks
Nowadays, digital videos have been widely leveraged to record and share various events and people’s daily life. It becomes urgent to provide automatic video semantic analysis and management for convenience. Shot boundary detection (SBD) plays a key fundamental role in various video analysis. Shot boundary detection aims to automatically detecting boundary frames of shots in videos. In this paper, we propose a progressive method for shot boundary detecting with histogram based shot filtering and C3D based gradual shot detection. Abrupt shots were detected firstly for its specialty and help alleviate locating shots across different shots by dividing the whole video into segments. Then, over the segments, gradual shot detection is implemented via a three-dimensional convolutional neural network model, which assign video clips into shot types of normal, dissolve, foi or swipe. Finally, for untrimmed videos, a frame level merging strategy is constructed to help locate the boundary of shots from neighboring frames. The experimental results demonstrate that the proposed method can effectively detect shots and locate their boundaries.
Shot boundary detection Shot transition Video indexing Convolutional neural networks Spatial-temporal feature
Lifang Wu Shuai Zhang Meng Jian Zhijia Zhao Dong Wang
Faculty of Information Technology,Beijing University of Technology,Beijing,China
国际会议
广州
英文
479-491
2018-11-23（万方平台首次上网日期，不代表论文的发表时间）