Performance Analysis of Shot Change Detection Methods

DOI

10.2991/iccasp-16.2017.78How to use a DOI?

Keywords

Shot change detection, Corner detection, Adaptive threshold.

Abstract

Shot change detection is a significant step in content based video indexing and retrieval. There are different types of transitions between the shots. Most of the shot change detection algorithms deal with these transitions separately. In this paper, we have carried out the analysis of shot change detection methods like pixel difference, histogram difference and Chi-square test and our proposed method. The proposed shot change detection method is integration of pre-processing and KLT (Kanade-Lucas-Tomasi) corner detection technique. In the pre-processing stage, adaptive local thresholding is used to eliminate non-boundary segments and only candidate segments are retained. The candidate segments are re ned using bisection-based comparisons to eliminate non-boundary frames. Only re ned candidate segments are preserved for further detections; hence, the speed of shot change detection is improved. KLT corner detection approach is used for obtaining key points in the frames from candidate segments. Shot change is detected if the key points between successive frames are not matching. Experimental results indicate that the proposed method is effective in terms accuracy and also helps in accelerating the shot change detection process, which can lead to better and fast retrieval of video.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - M. Banwaskar
AU  - A. Rajurkar
PY  - 2016/12
DA  - 2016/12
TI  - Performance Analysis of Shot Change Detection Methods
BT  - Proceedings of the International Conference on Communication and Signal Processing 2016 (ICCASP 2016)
PB  - Atlantis Press
SP  - 531
EP  - 539
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccasp-16.2017.78
DO  - 10.2991/iccasp-16.2017.78
ID  - Banwaskar2016/12
ER  -