Video Content Classification using Deep Learning

TL;DR

This paper proposes a deep learning model combining CNN and RNN for efficient video content classification, utilizing keyframe extraction to improve speed without losing accuracy.

Contribution

It introduces a novel keyframe extraction method integrated with CNN-RNN architecture for faster and accurate video classification.

Findings

Achieved high classification accuracy across multiple categories.

Reduced processing time by focusing on keyframes.

Demonstrated effectiveness of combined CNN-RNN with keyframe selection.

Abstract

Video content classification is an important research content in computer vision, which is widely used in many fields, such as image and video retrieval, computer vision. This paper presents a model that is a combination of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) which develops, trains, and optimizes a deep learning network that can identify the type of video content and classify them into categories such as "Animation, Gaming, natural content, flat content, etc". To enhance the performance of the model novel keyframe extraction method is included to classify only the keyframes, thereby reducing the overall processing time without sacrificing any significant performance.