Strategy for Foreground Movement Identification Adaptive to Background Variations

Abstract

<p>Video processing has gained a lot of significance because of its applications in various areas of research. This includes monitoring movements in public places for surveillance. Video sequences from various standard datasets such as I2R, CAVIAR and UCSD are often referred for video processing applications and research. Identification of actors as well as the movements in video sequences should be accomplished with the static and dynamic background. The significance of research in video processing lies in identifying the foreground movement of actors and objects in video sequences. Foreground identification can be done with a static or dynamic background. This type of identification becomes complex while detecting the movements in video sequences with a dynamic background. For identification of foreground movement in video sequences with dynamic background, two algorithms are proposed in this article. The algorithms are termed as Frame Difference between Neighboring Frames using Hue, Saturation and Value (FDNF-HSV) and Frame Difference between Neighboring Frames using Greyscale (FDNF-G). With regard to F-measure, recall and precision, the proposed algorithms are evaluated with state-of-art techniques. Results of evaluation show that, the proposed algorithms have shown enhanced performance.</p>

Eulerian video magnification: a review

“…Temporal filtering can also isolate a signal of interest because various motions occur at different temporal frequencies Narrowband linear filters in the temporal domain improve the signal-to-noise ratios for motions, such as respiration and vibrations, that particularly occur in the narrow range of frequencies. These filters can also separate object motions [53] that correspond to various frequencies, such as pipe vibration, which vibrates at a certain set of modal frequencies. Each model frequency has a different vibration spatial pattern.…”