Title: Video summarisation based on motion estimation using speeded up robust features

Authors: Dipti Jadhav; Udhav Bhosle

Addresses: Department of Information Technology, Ramrao Adik Institute of Technology, Dr D Y Patil, Vidyanagar, Sector 7, Nerul, Navi Mumbai, Maharashtra, India ' Department of Electronics and Telecommunication, Rajiv Gandhi Institute of Technology, Versova, Mumbai, Maharashtra, India

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.

Keywords: video summarisation; motion estimation; keyframes; speeded up robust features; SURF; affine transformation; homography.

DOI: 10.1504/IJCVR.2019.104039

International Journal of Computational Vision and Robotics, 2019 Vol.9 No.6, pp.569 - 582

Received: 05 Jul 2018
Accepted: 19 Oct 2018

Published online: 09 Dec 2019 *

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