# Video Content Classification using Deep Learning

arXiv: https://arxiv.org/abs/2111.13813 · HTML: https://mlsanity.com/paper/2111.13813/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 9 references that are in the map.
- First posted on arXiv 27 Nov 2021.

## Builds on (most cited first, 1 of 1)

- [Efficient Video Classification Using Fewer Frames](https://mlsanity.com/paper/1902.10640/) (2019)

## Built on by (most cited first, 1 of 1)

- [Pig aggression classification using CNN, Transformers and Recurrent Networks](https://mlsanity.com/paper/2403.08528/) (2024)

## Abstract (arXiv)

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.