Video alignment to a common reference

2015 Spring.Includes bibliographical references.Handheld videos often include unintentional motion (jitter) and intentional motion (pan and/or zoom). Human viewers prefer to see jitter removed, creating a smoothly moving camera. For video analysis, in contrast, aligning to a fixed stable background is sometimes preferable. This paper presents an algorithm that removes both forms of motion using a novel and efficient way of tracking background points while ignoring moving foreground points. The approach is related to image mosaicing, but the result is a video rather than an enlarged still image. It is also related to multiple object tracking approaches, but simpler since moving objects need not be explicitly tracked. The algorithm presented takes as input a video and returns one or several stabilized videos. Videos are broken into parts when the algorithm detects background change and it becomes necessary to fix upon a new background. We present two techniques in this thesis. One technique stabilizes the video with respect to the first available frame. Another technique stabilizes the videos with respect to a best frame. Our approach assumes the person holding the camera is standing in one place and that objects in motion do not dominate the image. Our algorithm performs better than previously published approaches when compared on 1,401 handheld videos from the recently released Point-and-Shoot Face Recognition Challenge (PASC)

VIDEO BACKGROUND RETRIEVAL USING MOSAIC IMAGES

Content-based video retrieval is one of the most active and exciting research areas in the field of multimedia technology. In this paper, we present an approach for video background retrieval using mosaic images and a Support Vector Machine(SVM). The video is captured by a moving camera and a portion of the scene is visible at any time. The Kanade-Lucas-Tomasi(KLT) feature tracker is used to get the correspondences between consecutive images and the homography is calculated using these correspondences. We use the homography to construct the mosaic background image and a Mixture of Gaussian(MoG) background subtraction algorithm to remove the moving objects in the scene. An SVM is then applied to classify the mosaic background image. The experimental results show the efficiency and effectiveness of the proposed approach. 1