In this guide, you’ll learn how to implement real-time hand gesture recognition using 3D Convolutional Neural Networks (CNNs) with PyTorch. This method is not only cutting-edge but also user-friendly if you follow along the steps carefully.

What You Need Before You Start

• Pytorch: Make sure you have PyTorch installed. Use the following command:
• bash
• conda install pytorch torchvision cuda80 -c soumith
• Python 3: Ensure you are running Python 3.

Understanding the Code Structure

The implementation consists of two primary components:

• A Detector: A lightweight CNN that detects gestures.
• A Classifier: A deep CNN that classifies the detected gestures.

Think of this like a two-person team: one person (the Detector) observes the actions and signals when they see something worthwhile, while the other person (the Classifier) analyzes that action and categorizes it into specific gestures.

Preparing the Dataset

To get started, you will need video datasets like EgoGesture and NVIDIA Dynamic Hand Gesture Datasets. Here’s how to prepare the data:

EgoGesture Preparation

• Download videos from the official site.
• Run the following commands to generate N frames:

mkdir annotation_EgoGesture
python utilsego_prepare.py training trainlistall.txt all
python utilsego_prepare.py training trainlistall_but_None.txt all_but_None
python utilsego_prepare.py training trainlistbinary.txt binary
python utilsego_prepare.py validation vallistall.txt all
python utilsego_prepare.py validation vallistall_but_None.txt all_but_None
python utilsego_prepare.py validation vallistbinary.txt binary
python utilsego_prepare.py testing testlistall.txt all
python utilsego_prepare.py testing testlistall_but_None.txt all_but_None
python utilsego_prepare.py testing testlistbinary.txt binary

• Generate annotation file in JSON format:

python utilsegogesture_json.py annotation_EgoGesture all
python utilsegogesture_json.py annotation_EgoGesture all_but_None
python utilsegogesture_json.py annotation_EgoGesture binary

nvGesture Preparation

• Download videos from the official site.
• Run the following commands:

mkdir annotation_nvGesture
python utilsnv_prepare.py training trainlistall.txt all
python utilsnv_prepare.py validation vallistall.txt all
python utilsnv_json.py annotation_nvGesture all

Jester Preparation

• Download videos from the official site.
• Run the annotations:

python utilsjester_json.py annotation_Jester

Running the Code

To run the code for training or testing:

• For Offline Testing and Training: Use:

bash run_offline.sh

• For Online Testing: Use:

bash run_online.sh

Troubleshooting Tips

If you encounter issues while implementing this project, consider the following troubleshooting steps:

• Double-check your PyTorch installation.
• Ensure all datasets are downloaded correctly and are in the expected format.
• Check for any missing dependencies in your Python environment.

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Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions.
Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.