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Generally, a set of frames represents a video structure, clips, or scenes. A segmentation process including breaking down a video sequence into its main components should pre-processed video analysis-based classification methods. Recently, Deep Learning Models-based video analysis and classification approaches have been grown-up and developed to be more concise and convenient for modern technologies such as big data, cloud computing, video surveillance, and video summarization systems. This paper focuses on the knowledge related to deep learning-based methods to achieve object detection and tracking along with video sequences. Our revision presents and discusses various studies of video classification tasks. Further, the fundamental purpose of this research is to look at which of these techniques affected mainly the performance of video classification tasks and the main parameters required to design and implement an efficient video classification system with relative challenges. A comparison study is performed on various types of video classification models to highlight the strong points of each model with a comprehensive analysis of its performance evaluation based on accuracy metrics. [ABSTRACT FROM AUTHOR]