CVABS: Moving Object Segmentation with Common Vector Approach for Videos
This work addresses background modeling for real-time computer vision applications like security and monitoring, but it appears incremental as it builds on existing subspace methods.
The paper tackled moving object segmentation in videos by developing a new subspace-based background modeling algorithm using the Common Vector Approach with Gram-Schmidt orthogonalization, achieving successful results across all problem types on the CDNet2014 dataset with a self-learning feedback mechanism.
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.