Automatic Extraction of Man-Made Objects from Aerial and Space Images
Automatic Extraction of Man-Made Objects from Aerial and Space Images
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Automatic Panoramic Image Stitching using Invariant Features
International Journal of Computer Vision
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Machine learning for high-speed corner detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Image change detection algorithms: a systematic survey
IEEE Transactions on Image Processing
BRISK: Binary Robust invariant scalable keypoints
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Removing Atmospheric Turbulence via Space-Invariant Deconvolution
IEEE Transactions on Pattern Analysis and Machine Intelligence
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A system capable of demonstrating surveillance of a vast area for target tracking in real time has been a challenging problem and in this paper we present architecture for a real time, mosaicing and change detection system which proves to be an efficient solution for the same. A method of pipelining SURF (Speeded Up Robust Features) has been proposed for performing real time registration to generate panoramic view of a very large scene at far away distances. The pipelined architecture of the proposed system performs the computationally intensive SURF registration over 6 times faster than conventional SURF without any significant compromise in accuracy. The atmospheric turbulence may restrict accurate registration of images therefore a solution has been proposed which performs atmospheric turbulence restoration. Spatial information based object detection and tracking of slow moving objects in the mosaics of large areas is performed which ensures robust and accurate detection of changes in the scene. The proposed system works for large range PTZ cameras with both TI and CCD sensors in real world conditions, with fast panning and vast area of monitoring giving high target detection accuracy. The experimentation has been performed on varied datasets of both CCD and TI sensors and in different environmental conditions for both day and night scenarios. It has been observed from the experimental result that the proposed system outperforms similar systems such as [1, 13, 10] in terms of robustness, ability to handle diverse environments and overall system performance.