Recent research on large data processing have been actively carried out in the name of cloud computing. Video surveillance system must handle larger amounts of data in real time. Video surveillance systems in a cloud computing environment constantly need to handle larger amounts of data in order to recognize and track an object. The system requires a technique which can handle larger amounts of data in order to recognize and track an object by extracting the feature of the object. However, most object tracking approaches based on feature matching have a problem, showing high computational complexity and/or weak robustness in various environments. This paper proposes a robust object recognition and tracking method, which uses an advanced feature matching for use on real time environment. Our algorithm recognizes an object using invariant features, and reduces the dimension of a feature descriptor to deal with the problems. The experimental result shows that our method is faster and more robust than the traditional methods, as well as the proposed method that can detect and track a moving object accurately in various environments.
Recently, we can easily create high resolution images with digital cameras for high-performance and make use them at variety fields. Especially, the image stitching method which adjusts couple of images has been researched. Image stitching can be used for military purposes such as satellites and reconnaissance aircraft, and computer vision such as medical image and the map. In this paper, we have proposed fast image stitching based on improved SURF algorithm using meaningful features in the process of images matching after extracting features from scenery image. The features are extracted in each image to find out corresponding points. At this time, the meaningful features can be searched by removing the error, such as noise, in extracted features. And these features are used for corresponding points on image matching. The total processing time of image stitching is improved due to the reduced time in searching out corresponding points. In our results, the processing time of feature matching and image stitching is faster than previous algorithms, and also that method can make natural-looking stitched image.
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