Sign Language Detection using ACTION RECOGNITION with Python | LSTM Deep Learning Model
Updated: November 18, 2024
Summary
This video introduces the concept of real-time sign language detection using Python, Media Pipe, Tensorflow, and Keras to predict sign language signs. The speaker explains the process of collecting key points from hands, body, and face, training a deep neural network with LSTM layers, and using OpenCV for real-time prediction. They demonstrate model training, visualization, real-time prediction, and performance evaluation using metrics like confusion matrix and accuracy score, emphasizing stability and accuracy improvements for reliable action detections.
TABLE OF CONTENTS
Introduction to Sign Language Estimation
Ambitious Goal: Real-Time Sign Language Detection Flow
Key Models for Sign Language Detection
Data Collection and Preparation
Setting Up the Environment
Initializing Webcam and Making Detections
Extracting and Visualizing Key Points
Conclusion of Landmark Visualization
Setting Up Real-Time Landmark Detection
Rendering Landmarks
Enhancing Landmark Visualization
Customizing Landmark Colors
Optimizing Landmark Detection
Extracting Key Points
Concatenating Key Points
Creating Folders for Data Storage
Setting Up Folder Structure
Collecting Data
Logic Adjustment for Data Collection
Frame Collection and Logic Application
Visualization and Error Resolution
Data Saving and Review
Data Preprocessing
Model Training Preparation
LSTM Neural Network Setup
Building Dense Layers
Specifying Actions Layer
Neural Network Transition
Model Compilation and Training
Tensorboard Visualization
Model Evaluation
Real-time Testing and Rendering
Visualization Logic Update
Passing Arguments for Predicted Model
Drawing Dynamic Rectangle
Dynamic Bar Length Based on Probability
Outputting Text and Returning Frame
Real-Time Visualization Test
Wrap-Up and Applications
Debugging Sequence Append Issue
Updating Sequence Logic
Implementing Predictions Array
Testing Model Stability
Introduction to Sign Language Estimation
Introduction to the concept of sign language estimation using action detection, highlighting the transition from single frame detection to multi-frame prediction.
Ambitious Goal: Real-Time Sign Language Detection Flow
Setting the goal of developing a real-time sign language detection system using Python, Media Pipe, Tensorflow, and Keras to detect and predict sign language signs.
Key Models for Sign Language Detection
Explanation of the key models used, including Media Pipe Holistic for extracting key points and a LSTM model built with Tensorflow and Keras for predicting sign language signs.
Data Collection and Preparation
Process of collecting data on key points from hands, body, and face, storing them as numpy arrays, training a deep neural network with LSTM layers, and using OpenCV for real-time prediction.
Setting Up the Environment
Installation of necessary dependencies including TensorFlow, TensorFlow GPU, OpenCV, Media Pipe, Scikit-learn, and Matplotlib for building the sign language detection system.
Initializing Webcam and Making Detections
Instructions on accessing the webcam, capturing frames, processing images, and making detections using Media Pipe Holistic on key points like face, hands, and body parts.
Extracting and Visualizing Key Points
Steps to extract key points from Media Pipe Holistic model, including how to draw landmarks on the image frames to visualize face, hand, and pose landmarks.
Conclusion of Landmark Visualization
Finalizing the visualization of landmarks on images, understanding the connection map for different body parts, and applying the drawn landmarks on the frames.
Setting Up Real-Time Landmark Detection
The process of setting up real-time landmark detection by drawing landmarks and applying formatting to the video stream.
Rendering Landmarks
Adjusting the rendering settings to display the detected landmarks on the screen in real-time.
Enhancing Landmark Visualization
Applying formatting and visual enhancements to the detected landmarks for better visibility and distinction.
Customizing Landmark Colors
Customizing the colors and styles of the detected landmarks to differentiate between hand, face, and pose connections.
Optimizing Landmark Detection
Fine-tuning the landmark detection process by updating the formatting and color specifications of the landmarks.
Extracting Key Points
Extracting key points such as hand, face, and pose landmarks for sign language detection using MediaPipe Holistic models.
Concatenating Key Points
Combining and flattening extracted key points into a structured array for further processing in sign language detection.
Creating Folders for Data Storage
Setting up folders to store extracted data and organizing them by actions and sequences for efficient data management.
Setting Up Folder Structure
The speaker sets up a folder structure by creating folders for different actions and sequences. The process involves checking if folders are already created and skipping them, creating new directories, and ensuring the correct folder structure is in place.
Collecting Data
The speaker explains the process of collecting data by taking snapshots at each point in time, looping through actions and videos, and collecting key points from each frame. They discuss the logic of taking breaks between videos and frames to ensure data collection is consistent.
Logic Adjustment for Data Collection
The speaker adjusts the logic for data collection by looping through actions, sequences, and frames. They focus on collecting key points per video and applying break logic for efficient data collection.
Frame Collection and Logic Application
The speaker elaborates on the process of collecting frames, applying logic to take breaks between videos, and ensuring smooth data collection. They discuss the importance of pausing between video captures for accurate data collection.
Visualization and Error Resolution
The speaker addresses issues related to visualization, error correction, and adjusting parameters for smooth data collection. They focus on the importance of clear visualization during frame collection and address errors encountered during the process.
Data Saving and Review
The speaker explains the process of saving data as numpy arrays, reviewing the saved arrays, and loading them back for analysis. They discuss the significance of saving frames as numpy arrays for further processing and analysis.
Data Preprocessing
The speaker discusses the preprocessing of collected data, including structuring key point sequences, saving frames in numpy arrays, and preparing data for training and testing partition. They emphasize the importance of preprocessing data before model training.
Model Training Preparation
The speaker prepares for model training by importing dependencies, creating a label map, structuring feature data, creating labels, and dividing data into training and testing sets. They focus on setting up the data for training a LSTM neural network.
LSTM Neural Network Setup
The speaker sets up a LSTM neural network architecture by defining sequential, LSTM, and dense layers. They explain the purpose and settings of each layer, specifying the input shape, units, activation function, and return sequences for efficient model training.
Building Dense Layers
Explanation of adding dense layers with specified units and activation functions.
Specifying Actions Layer
Description of the final dense layer for actions prediction with softmax activation function.
Neural Network Transition
Discussion on transitioning from CNN to MediaPipe Holistic combined with LSTM Lasers for better performance.
Model Compilation and Training
Process of compiling the model with optimizer and loss function and fitting the model.
Tensorboard Visualization
Setting up and accessing TensorBoard for model training visualization.
Model Evaluation
Importing metrics from scikit-learn to evaluate model performance with confusion matrix and accuracy score.
Real-time Testing and Rendering
Implementing real-time prediction logic, rendering detections, and saving and reloading the model.
Visualization Logic Update
Updating visualization logic for improved prediction display and probability visualization.
Passing Arguments for Predicted Model
Passing through positional arguments such as actions, input frame (image), and color to get results from the predicted model.
Drawing Dynamic Rectangle
Using cv2.rectangle to draw a dynamic rectangle on the output frame based on the position or action being worked on with dynamic positioning calculations.
Dynamic Bar Length Based on Probability
Adjusting the length of a bar dynamically using the probability values to indicate the probability of different actions such as hello, thanks, and I love.
Outputting Text and Returning Frame
Outputting text using cv2.putText method and returning the output frame after dynamic adjustments based on probabilities and actions.
Real-Time Visualization Test
Testing the real-time visualization of probabilities for different actions like hello, thanks, and I love you, and observing the detection performance in real time.
Wrap-Up and Applications
Summarizing the key points covered in the video, including the installation of dependencies, building functions, extracting key points, processing data, making predictions, and real-time testing, and highlighting the various applications of the model.
Debugging Sequence Append Issue
Explaining the debugging process to resolve issues related to sequence.append and sequence.insert logic to ensure correct frame selection for action detection.
Updating Sequence Logic
Demonstrating the correct sequence.append and sequence.insert logic to ensure the accurate selection of frames for action detection, improving stability and performance.
Implementing Predictions Array
Implementing a predictions array to enhance stability by checking the consistency of predictions in the last 10 frames for more accurate and stable detections.
Testing Model Stability
Testing the model stability and accuracy improvements by incorporating the predictions array logic, resulting in more stable and reliable action detections.
FAQ
Q: What are the key components involved in developing a real-time sign language detection system using Python?
A: The key components involved in developing a real-time sign language detection system using Python include Media Pipe, Tensorflow, Keras, and OpenCV.
Q: What are the key models used in the sign language detection system?
A: The key models used in the sign language detection system include Media Pipe Holistic for extracting key points and a LSTM model built with Tensorflow and Keras for predicting sign language signs.
Q: How is the data collected for the sign language detection system?
A: The data for the sign language detection system is collected by capturing key points from hands, body, and face, storing them as numpy arrays, and training a deep neural network with LSTM layers.
Q: What dependencies are necessary for building the sign language detection system?
A: The necessary dependencies for building the sign language detection system include TensorFlow, TensorFlow GPU, OpenCV, Media Pipe, Scikit-learn, and Matplotlib.
Q: What is the importance of taking breaks between videos and frames during data collection?
A: Taking breaks between videos and frames during data collection ensures data collection consistency and accuracy.
Q: How is the data preprocessed before model training in the sign language detection system?
A: The data is preprocessed by structuring key point sequences, saving frames as numpy arrays, and preparing the data for training and testing partition.
Q: What is the purpose of the LSTM neural network in the sign language detection system?
A: The LSTM neural network in the sign language detection system is used for predicting sign language signs by processing sequential data.
Q: How are predictions stabilized in the sign language detection system?
A: Predictions are stabilized by implementing a predictions array to check the consistency of predictions in the last 10 frames for more accurate and stable detections.
Q: What tools are used for evaluating the model performance in the sign language detection system?
A: Metrics from scikit-learn such as confusion matrix and accuracy score are used for evaluating the model performance.
Q: How is real-time prediction achieved in the sign language detection system?
A: Real-time prediction is achieved by rendering detections, adjusting visualization logic based on probabilities, and dynamically updating the output frame.
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