The Detection of Video Shot Transitions Based on Primary Segments Using the Adaptive Threshold of Colour-Based Histogram Differences and Candidate Segments Using the SURF Feature Descriptor

Abstract

Aim: Advancements in multimedia technology have facilitated the uploading and processing of videos with substantial content. Automated tools and techniques help to manage vast volumes of video content. Video shot segmentation is the basic symmetry step underlying video processing techniques such as video indexing, content-based video retrieval, video summarization, and intelligent surveillance. Video shot boundary detection segments a video into temporal segments called shots and identifies the video frame in which a shot change occurs. The two types of shot transitions are cut and gradual. Illumination changes, camera motion, and fast-moving objects in videos reduce the detection accuracy of cut and gradual transitions. Materials and Methods: In this paper, a novel symmetry shot boundary detection system is proposed to maximize detection accuracy by analysing the transition behaviour of a video, segmenting it initially into primary segments and candidate segments by using the colour feature and the local adaptive threshold of each segment. Thereafter, the cut and gradual transitions are fine-tuned from the candidate segment using Speeded-Up Robust Features (SURF) extracted from the boundary frames to reduce the algorithmic complexity. The proposed symmetry method is evaluated using the TRECVID 2001 video dataset, and the results show an increase in detection accuracy. Result: The F1 score obtained for the detection of cut and gradual transitions is 98.7% and 90.8%, respectively. Conclusions: The proposed symmetry method surpasses recent state-of-the-art SBD methods, demonstrating increased accuracy for both cut and gradual transitions in videos.