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detecting randr (monitor) changes
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an error occurred while detecting silences.
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abstract video stitching has become more popular nowadays, since it provides a wide high resolution panoramic videos. these panoramic videos are generated by stitching of two or more individual videos with certain overlapping similarities. video stitching faces challenging problems such as misalignment of frames due to camera angle, ghost effects that rise due to visual parallax, illumination variations in the two videos chosen for stitching. various algorithms are used for image stitching. scale invariant feature transform (sift) is one among the different algorithms. sift is a feature descriptor, used for detecting, extracting and matching the features. the feature used here is keypoints. the random sample consensus (ransac) is performed on the matching point sets to remove outliers and finds the transformation between the frames present in the videos. it is followed by warping, gain compensation and blending. warping is used for mapping pixels from one plane to the other and it is utilized to align the frames correctly. gain compensation minimizes the intensity difference of overlapping pixels exposure difference between the two video frames can be equalized using this technique. blending smoothens the transition between the frames. hence the frames/images in the two videos are stitched to create a panoramic video with a wide field-of-view and high-resolution video. from the stitched video human tracking is performed. human tracking is an essential requirement for human-centric surveillance, activity recognition, gait recognition etc. background modelling is used here along with the kalman filter to avoid the problem of occlusion. the system has been implemented using c++ and the opencv library.
at times you prefer quietness