Video Classification for Video Service Providers : A Survey

Abstract

One of the very interesting data modalities is video. From a dimensionality and size perspective, videos are one of the most interesting and intuitive data types which enable fast and easy object recognition and learning. Video classification is an important task for archiving digital contents for various video service providers. Video uploading platforms such as YouTube are collecting enormous datasets, empowering Deep Learning research. Videos being an important source to recognize any activity by the humans, video classification becomes an important and critical job for video service providers. The survey paper studies various deep learning, transfer learning and hybrid model approaches. Video data normally occurs as continuous, analog signals In order for a computer to process this video data, the analog signals must be converted to a non-continuous, digital format. In a digital format, the video data can be stored as a series of bits on a hard disk or in computer memory. A video sequence is displayed as a series of frames. Each frame is a snapshot of a moment in time of the motion-video data, and is very similar to a still image. When the frames are played back in sequence on a display device, a rendering of the original video data is created. In real-time video the playback rate is 30 frames per second. This is the minimum rate necessary for the human eye to successfully blend each video frame together into a continuous, smoothly moving image. A single frame of video data can be quite large in size. A video frame with a resolution of 512 x 482 will contain 246,784 pixels. If each pixel contains 24 bits of color information, the frame will require 740,352 bytes of memory or disk space to store. Assuming there are 30 frames per second for real-time video, a 10-second video sequence would be more than 222 megabytes in size! It is clear there can be no computer video without at least one efficient method of video data compression.