In the rapidly evolving field of computer vision, video classification has become a focal area of research and application. With numerous methodologies available, choosing the appropriate one can be a daunting task. In this blog post, we’ll explore five distinct video classification methods that employ different architectures to achieve remarkable results. Let’s dive in!

Overview of Video Classification Methods

• Method 1: Classify one frame at a time using a Convolutional Network (ConvNet).
• Method 2: Extract features from each frame with a ConvNet, passing the sequence to a Recurrent Neural Network (RNN) using a separate network.
• Method 3: Utilize a time-distributed ConvNet, passing the features through an RNN, functioning similarly to Method 2, but all integrated into one network (this architecture is referred to as the LRCN network).
• Method 4: Extract features from each frame with a ConvNet and pass the sequences to a Multi-Layer Perceptron (MLP).
• Method 5: Make use of a 3D convolutional network, which has two versions of 3D convolution available.

Requirements

Before you begin, ensure that you have the following in place:

• Keras 2 and TensorFlow 1 or greater installed. You can check the requirements in the requirements.txt file.
• To install the necessary libraries, run:

pip install -r requirements.txt

• ffmpeg must be installed for extracting video files. If ffmpeg is not in your system path, update the path in data2_extract_files.py.

Getting the Data

To get started, download the UCF dataset and prepare the necessary folders:

cd data
wget http://crcv.ucf.edu/data/UCF101/UCF101.rar
unrar e UCF101.rar
mkdir train
mkdir test
mkdir sequences
mkdir checkpoints

Once the folders are created, run the following scripts to organize the videos, extract their frames, and create a CSV file:

python 1_move_files.py
python 2_extract_files.py

Extracting Features

Before diving into the LSTM and MLP models, you’ll need to extract features from the video frames using a CNN. Execute the following script:

python extract_features.py

This process is resource-intensive and may take a while; on a Dell machine with a GeForce 960m GPU, it can take approximately 8 hours. If you’re interested in limiting the processing to the first N classes, there’s an option in the extract_features.py file.

Training the Models

You can train the chosen models by running the following scripts:

• The CNN-only method (Method #1):

python train_cnn.py

• For the remaining models, use:

python train.py

Configuration options are available in the train.py file to select which model to run. Remember that all models are defined in models.py, so refer to this file to see the compatible models for training.

Training logs will be saved in CSV format and also in TensorBoard files. To monitor your progress while training, run the command:

tensorboard --logdir=data/logs

Demo and Future Improvements

As of now, a demo feature allowing input of a video file for predictions has not been implemented. However, contributions to enhance this project are welcome!

Future improvements include:

• Add data augmentation techniques to combat overfitting.
• Support multiple workers in the data generator for faster training.
• Add a demo script.
• Support additional datasets.
• Implement optical flow and more complex network architectures.

Troubleshooting

Should you encounter issues during implementation, consider the following troubleshooting tips:

• Ensure all required packages are installed and updated.
• Double-check that the dataset has been downloaded and extracted correctly.
• Verify the paths specified in any script align with the file paths on your system.
• If ffmpeg is not recognized, ensure it is installed and correctly set in your system path.
• For further assistance and collaboration opportunities, for updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that advancements in methods like video classification are crucial for the future of AI, enabling more comprehensive and effective solutions. Our team continually explores novel methodologies to push the envelope in artificial intelligence, ensuring our clients benefit from the latest technological innovations.

By understanding these five methods, you’re one step closer to implementing robust video classification solutions tailored to your specific needs!