A sports training video classification model based on deep learning is studied for targeting low accuracy caused by the randomness of objective movement in video. The camera calibration technology used to restore position target real three-dimensional space. After video, preprocessed. input segment divided into equal length segments obtain subvideo segment. motion vector field, brightness featu...

Background: With the proliferation of machine learning and its applications in a variety spheres that are important to humans their day-to-day lives, there is pressing need for automatic detection models can identify abnormal behaviors or acts violence. Methods: This study examines model uses ensemble boosting histograms oriented gradients (HOG) detect violent content from feature vector with s...

We investigate video classification via a 3D deep convolutional neural network (CNN) that directly ingests compressed bitstream information. This idea is based on the observation that video macroblock (MB) motion vectors (that are very compact and directly available from the compressed bitstream) are inherently capturing local spatio-temporal changes in each video scene. Our results on two stan...

The rise of useful multimedia applications has sparked a rush to perfect video compression methods. Over the last decade, there been lot interest in using motion-compensated frame interpolation (MCFI) methods boost rate during playback both academic and consumer electronics sectors. Interpolation refers process defining values function at places that are not represented by any examples. It acco...