Video summarisation based on motion estimation using speeded up robust features Online publication date: Mon, 09-Dec-2019
by Dipti Jadhav; Udhav Bhosle
International Journal of Computational Vision and Robotics (IJCVR), Vol. 9, No. 6, 2019
Abstract: Video summarisation (VS) is a technique to extract keyframes from a video based on video contents. It provides user with a brief representation of video contents to semantically understand the video. This paper aims to present video summarisation based on motion between consecutive video frames. The motion between frames is represented by affine and homograph transformation. The video frames are represented by a set of speeded up robust features (SURF). The keyframes are extracted in a sequential manner by successively comparison with the previously declared keyframe based on motion. The validity of the proposed algorithms is demonstrated on videos from Internet, YouTube dataset and Open Video Project. The proposed work is evaluated by comparing it with different classical and state-of-the-art video summarisation methods reported in the literature. The experimental results and performance analysis validates the effectiveness and efficiency of the proposed algorithms.
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