LIBRAS Decoder: Tracking and Recognizing Gesture Alphabet

Most of gesture alphabet detectors establish a fixed bounding box to capture the hand gesture and further classify it into the corresponding sign. Nevertheless, such an approach might be inefficient when dealing with real-world scenarios as users should freely express their gestures without being contained in a marking box. Therefore, our approach relies on a hand-tracking system that can freely detect a human hand, establish its bounding box, and perform the classification over the expressed gesture.

This repository is a work in progress, and we intend to keep fostering accessibility-based research.

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Structure

• classifiers
  • small.py: A small-sized Convolutional Neural Network, used for easy tasks;
  • medium.py: A medium-sized Convolutional Neural Network, used for more complex tasks;
• core
  • classifier.py: Defines the classifier class, used to recognize the gestures;
  • detector.py: Defines the detector class, used to track the hands;
  • stream.py: Defines the stream class, used to stream a video from an input webcam;
• data: A place that gathers datasets used in the classification task;
• models: A place that holds pre-trained detection and classification models;
• utils
  • compressor.py: Helper that eases the compression and de-compression of .tar.gz files;
  • constants.py: Constants definitions;
  • dictionary.py: Dictionary that maps between classes and labels;
  • loader.py: Loads classification-based datasets into generators;
  • processor.py: Processing utilities, such as detecting bound boxes, drawing labels and creating binary masks.

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Package Guidelines

Installation

Install all the pre-needed requirements using:

pip install -r requirements.txt

Environment configuration

Check the utils/constants.py file in order to correct set where data and pre-trained models should be saved and loaded.

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Usage

Pre-Train Hand Detection Models

The first step to recognize the gesture alphabet lies in identifying and tracking hand's movement. To accomplish such a procedure, one needs to pre-train its hand detection model (usually performed with the Egohands dataset).

Train and Evaluate Classification Model

After detecting and tracking the hand's movement, it is possible to snapshot the hand and pass it through a classification network, such as a Convolutional Neural Network. To accomplish such a step, one needs to download the LIBRAS dataset provided by LASIC/UEFS.

With the dataset in hands, one can pre-train its classification model using the following script:

python train_classifier.py -h

Additionally, as an optional procedure, one can evaluate its classification model, as follows:

python evaluate_classifier.py -h

Note that -h invokes the script helper, which assists users in employing the appropriate parameters.

Detect and Classify a Video Stream

Finally, it is now possible to stream a video from the webcam and perform real-time detection and classification. Use the following script to accomplish such a procedure:

python detect_classify_stream.py -h

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Acknowledgements

We are glad to acknowledge two important sources for conducting our research, as follows:

• TF Object Detection API Wrapper: https://github.com/jkjung-avt/hand-detection-tutorial

• LIBRAS Dataset: I. L. O. Bastos, M. F. Angelo and A. C. Loula, Recognition of Static Gestures Applied to Brazilian Sign Language (Libras). 28th SIBGRAPI Conference on Graphics, Patterns and Images, Salvador, 2015, pp. 305-312.

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Support

We know that we do our best, but it is inevitable to acknowledge that we make mistakes. If you ever need to report a bug, report a problem, talk to us, please do so! We will be available at our bests at this repository.

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