Machine learning and deep learning have achieved many successes in problem-solving applications in many areas in recent years. Deep learning in which the Convolutionary Neural Network (CNN) have a powerful model to solve the problem of image recognition in decades of image recognition. With the internet and intelligent device growth speed up, video data on media, storage and cloud are explosive. The data information so enormous that video must be analyzed, translated and understood for many progress such as video processing, recommendation, content identification, filter content, etc. While normal method is not good for classifying action, video type and video analysis, the CNN model with a straight network can reverse the act into a simple process. CNN model research focuses on how to classify video or analyze video.

One of these is the C3D model that uses convolution 3D to obtain spatio-temporal data feature. Image below interpretation of the convolution operation 1d, 2d and 3d in the field of image processing.

Using the 3D convolutions in video, we can extract features from the video dataset. The model would perform better if the action was classified in combination with linear SVM.

With the purpose of the model, we can recognize the type of video. At next step, we will then process transcoding to get specific variable parameters to improve the quality of the video. As a result, we can profoundly change the bitrate of the video following each type of video.

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However, the model is not the only one can get benefit from this technology. There are also other ones which can also gets some advantage at some point. Assuming you have a decent amount of expertise in CNN model knowledge, the C3D model can be understood in logical manner.


Credit to: Van Hoang Cong (