Keyframe extraction using binary robust invariant scalable keypoint features

Ashish Khare; B. Reddy Mounika; Manish Khare

doi:10.1117/12.2559105

31 January 2020 Keyframe extraction using binary robust invariant scalable keypoint features

Ashish Khare, B. Reddy Mounika, Manish Khare

Proceedings Volume 11433, Twelfth International Conference on Machine Vision (ICMV 2019); 1143308 (2020) https://doi.org/10.1117/12.2559105
Event: Twelfth International Conference on Machine Vision, 2019, Amsterdam, Netherlands

Abstract

In recent years, research in the field of keyframe extraction become more attractive due to its use in advanced applications like video surveillance. In this paper, we introduce a novel algorithm of keyframe extraction which utilizes Binary Robust Invariant Scalable Keypoint features to obtain the dissimilarity level of consecutive frames and establishes shot transition boundary, from where we extract keyframes. The frame at which dissimilarity level is high is taken as a keyframe. The proposed algorithm is tested on ten different videos of animation category. Performance of the method is assessed using the evaluation metrics- Figure of merit, Detection percentage, Accuracy and missing factor. The experimental results and analysis shows improved performance of the proposed algorithm over the other state-ofthe-art methods.

Citation Download Citation

Ashish Khare, B. Reddy Mounika, and Manish Khare "Keyframe extraction using binary robust invariant scalable keypoint features", Proc. SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 1143308 (31 January 2020); https://doi.org/10.1117/12.2559105

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