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

Fast Pixel-Based Video Scene Change Detection

Abstract- This paper proposes a new simple and efficient method to detect abrupt scene change based on only pixel values. Conventional pixel-based techniques can produce a significant number of false detections and missed detections when high motion and brightness variations are present in the video. To increase scene change detection accuracy yet maintaining a low computational complexity, a two-phase strategy is developed. Frames are firstly tested against the mean absolute frame differences (MAFD) via a relaxed threshold, which rejects about 90 % of the non-scene change frames. The rest 10 % of the frames are then normalized via a histogram equalization process. A top-down approach is applied to refine the decision via four features: MAFD and three other features based on normalized pixel values- signed difference of mean absolute frame difference (SDMAFD*), absolute difference of frame variance (ADFV*), and mean absolute frame differences (MAFD*). Experimental results show that our method contributes to higher detection rate and lower missed detection rate while maintaining a low computational complexity, which is attractive for real-time video applications. I