Video shot boundary detection based on candidate segment selection and transition pattern analysis

Video shot boundary detection or shot segmentation is an integral part of semantic video analysis. The objective of this process is to automatically detect the boundary region in video that further segment the video into meaningful shot, scene and so on. Video frame feature representation therefore plays an important role in the process where it directly affects the overall performance of the system. The transition points between meaningful scenes can be emphasised by the extracted features. In this paper, a combination of global and local feature descriptors is implemented to represent the temporal characteristic in video. Motivated by computational efficiency and practical implementation, a video shot boundary detection scheme using adaptive thresholding is proposed. Candidate segment selection and transition pattern analysis are implemented by the dissimilarity score between video frames. The performance evaluation is constructed on a golf video dataset using the precision and recall performance measures.