Comparative Study of Motion Amplification Techniques for Video Sequences

Abstract

The purpose of this paper is to review Motion Amplification Techniques in video sequences. Various computational techniques are used to visualize, analyze and efficiently represent imperceptible variation in image and video sequences. These computational methods which are used reveal temporal changes in videos which are not visible to the naked eyes. In these techniques standard video sequence is used as an input on which spatial decomposition is applied then temporal filtering is applied. The filtered output is then amplified to expose the invisible content in the input video. This paper contains four techniques which are used for enhancement of temporal variations in the video: 1. Linear Approximation Method, Eulerian Video Magnification (EVM), 2. Phase Based Video Processing 3. Fast Phase Based Video Processing (Riesz Pyramid) and 4. Enhanced Eulerian Video Magnification (EEVM). Using these techniques, it is possible to magnify and see tiny motions and subtle color variations.

After review it is observed that, the noise level in EVM technique is more which reduced in EEVM technique using motion analysis and image warping. The other two phase based techniques supports larger amplification factor and better noise performance.