Motion Detection Using a Hybrid Texture-Based Approach

Motion analysis plays an important role in various real-time applications like object detection, human-computer interaction, surveillance systems, human detection and tracking, event monitoring, etc. Background subtraction that aims at separating the motion regions from the static portions lays the foundation of all such applications. Most of the background subtraction techniques developed to date explore colour features of pixels, either individually or in a spatio-temporal manner. Many other techniques exploit texture characteristics of pixels, while a few have been developed that employ a combination of both texture and colour characteristics for extracting motion-related information from frames. But most of the efficient background modelling techniques demand extensive use of hardware and computation. In this paper, we propose a hybrid sample consensus-based foreground segmentation technique that fuses similarity-based binary patterns of pixels with YCbCr colour space. The core of a pixel-based technique has been reconstructed to obtain drastically refined results.