CVABS: Moving Object Segmentation with Common Vector Approach for Videos

Background modelling is a fundamental step for several real-time computer vision applications that requires security systems and monitoring. An accurate background model helps detecting activity of moving objects in the video. In this work, we have developed a new subspace based background modelling algorithm using the concept of Common Vector Approach with Gram-Schmidt orthogonalization. Once the background model that involves the common characteristic of different views corresponding to the same scene is acquired, a smart foreground detection and background updating procedure is applied based on dynamic control parameters. A variety of experiments is conducted on different problem types related to dynamic backgrounds. Several types of metrics are utilized as objective measures and the obtained visual results are judged subjectively. It was observed that the proposed method stands successfully for all problem types reported on CDNet2014 dataset by updating the background frames with a self-learning feedback mechanism.Comment: 12 Pages, 4 Figures, 1 Tabl

Robust Foreground Segmentation Based on Two Effective Background Models

Foreground segmentation is a common foundation for many computer vision applications such as tracking and behavior analysis. Most existing algorithms for foreground segmentation learn pixel-based statistical models, which are sensitive to dynamic scenes such as illumination change, shadow movement, and swaying trees. In order to address this problem, we propose two block-based background models using the recently developed incremental rank-(R1, R2, R3) tensor-based subspace learning algorithm (referred to as IRTSA [1]). These two IRTSA-based background models (i.e., IRTSAGBM and IRTSA-CBM respectively for grayscale and color images) incrementally learn low-order tensor-based eigenspace representations to fully capture the intrinsic spatio-temporal characteristics of a scene, leading to robust foreground segmentation results. Theoretic analysis and experimental evaluations demonstrate the promise and effectiveness of the proposed background models.Xi Li, Weiming Hu, Zhongfei Zhang, Xiaoqin Zhanghttp://press.liacs.nl/mir2008/index.htm

Change detection in combination with spatial models and its effectiveness on underwater scenarios

This thesis proposes a novel change detection approach for underwater scenarios and combines it with different especially developed spatial models, this allows accurate and spatially coherent detection of any moving objects with a static camera in arbitrary environments. To deal with the special problems of underwater imaging pre-segmentations based on the optical flow and other special adaptions were added to the change detection algorithm so that it can better handle typical underwater scenarios like a scene crowded by a whole fish swarm

Principal Component Analysis

This book is aimed at raising awareness of researchers, scientists and engineers on the benefits of Principal Component Analysis (PCA) in data analysis. In this book, the reader will find the applications of PCA in fields such as image processing, biometric, face recognition and speech processing. It also includes the core concepts and the state-of-the-art methods in data analysis and feature extraction