This paper presents a novel and efficient texture-based method for modeling the background and detecting moving objects from a video sequence. Each pixel is modeled as a group of adaptive local binary pattern histograms that are calculated over a circular region around the pixel. The approach provides us with many advantages compared to the state-of-the-art. Experimental results clearly justify our model. Index TermsMotion, texture, background subtraction, local binary pattern.

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Abstract. Local feature detection and description have gained a lot of interest in recent years since photometric descriptors computed for interest regions have proven to be very successful in many applications. In this paper, we propose a novel interest region descriptor which combines the strengths of the well-known SIFT descriptor and the LBP texture operator. It is called the center-symmetric local binary pattern (CS-LBP) descriptor. This new descriptor has several advantages such as tolerance to illumination changes, robustness on flat image areas, and computational efficiency. We evaluate our descriptor using a recently presented test protocol. Experimental results show that the CS-LBP descriptor outperforms the SIFT descriptor for most of the test cases, especially for images with severe illumination variations.

The detection of moving objects from video frames plays an important and often very critical role in different kinds of machine vision applications including human detection and tracking, traffic monitoring, humanmachine interfaces and military applications, since it usually is one of the first phases in a system architecture. A common way to detect moving objects is background subtraction. In background subtraction, moving objects are detected by comparing each video frame against an existing model of the scene background. In this paper, we propose a novel block-based algorithm for background subtraction. The algorithm is based on the Local Binary Pattern (LBP) texture measure. Each image block is modelled as a group of weighted adaptive LBP histograms. The algorithm operates in real-time under the assumption of a stationary camera with fixed focal length. It can adapt to inherent changes in scene background and can also handle multimodal backgrounds.

This paper presents a fully automatic image mosaicing method for needs of wide-area video surveillance. A pure featurebased approach was adopted for finding the registration between the images. This approach provides us with several advantages. Our method is robust against illumination variations, moving objects, image rotation, image scaling, imaging noise, and is relatively fast to calculate. We have tested the performance of the proposed method against several video sequences captured from real-world scenes. The results clearly justify our approach.