Classifying Video based on Automatic Content Detection Overview
This is an incremental literature review and experimental research for video analysis in computer vision.
The paper tackles video classification by reviewing state-of-the-art methods for multi-label classification and aims to develop a method to handle sequential frame data based on automatic content detection.
Video classification and analysis is always a popular and challenging field in computer vision. It is more than just simple image classification due to the correlation with respect to the semantic contents of subsequent frames brings difficulties for video analysis. In this literature review, we summarized some state-of-the-art methods for multi-label video classification. Our goal is first to experimentally research the current widely used architectures, and then to develop a method to deal with the sequential data of frames and perform multi-label classification based on automatic content detection of video.