video classification cnn rnn

Introduction

This document provides an overview of video classification using a CNN-RNN architecture, primarily utilizing the UCF101 dataset. The model is built using TensorFlow Keras and demonstrates the integration of transfer learning with recurrent models to classify video actions.

Architecture

The video classifier leverages a hybrid architecture combining Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). CNNs are utilized for spatial information processing, while RNNs, specifically GRU layers, handle temporal information. This approach effectively models the ordered sequence of video frames to recognize actions such as "cricket shot," "punching," and more.

Training

The model is trained using the UCF101 dataset, which contains videos categorized by different actions. Transfer learning is applied to enhance the model's performance. The training process focuses on both spatial and temporal aspects of video data to develop a robust action recognition system.

Guide: Running Locally

1. Setup Environment: Ensure you have Python and TensorFlow installed. You may also need additional libraries such as Keras and any dependencies listed in the project requirements.

2. Download Dataset: Obtain the UCF101 dataset and prepare it for use. This might involve downloading the videos and organizing them according to the provided labels.

3. Clone Repository: Clone the project repository to your local machine using Git.

   git clone <repository_url>
   cd video-classification-cnn-rnn
   

4. Run Training Script: Execute the training script provided in the repository, ensuring that all paths are correctly set to your dataset location.

5. Cloud GPU Suggestion: For faster training, consider using cloud GPU services like Google Colab, AWS EC2, or Azure. These platforms offer powerful GPUs that can significantly reduce training time.

License

The model and associated code are subject to the licensing agreements as specified in the project's repository. Ensure compliance with these terms when using or modifying the code.