We trained an LSTM model with gestures from John 3:16 and John 14:6. Here is a demo of the model inference. Our colleague is doing the gesture for the entire John 4:16 verse. The model correctly predicts 6 out of 7 gestures
Trained 8 classes namely ‘Yes’, ‘No’, ‘Hello’, ‘Thank You’, ‘Sorry’, ‘What is your name’, ‘Are you deaf’, ‘Nice to meet you’. Extracted 6 (X, Y) coordinates from shoulders, elbows and wrists positions from openPose to build feature vectors from a single frame. In a 2-second training video, we extracted 12 frames and generated the feature matrix. We used a total of 400 training videos and with this feature matrix, we trained an LSTM model to build a classifier.
This is a demo of a computer vision algorithm that recognizes human activity and gestures. Neural Networks are special software algorithms that mimic human brain. Our team utilized the recent advances in training these Neural Networks to train our human activity recognition models. We had lot of fun training these models and also learned a lot. We learned how to optimize these models so that they can run in inexpensive hardware. How to make these models small enough to run locally without the need to send the video feed to an external device. This greatly increases the privacy. These models are in useful in many situations like productivity measurements, Injury prevention etc., Our goal is to help our customers automate safety and monitoring tasks to safe money and improve productivity.
This is a demo of training a Convolutional 3D Neural Network to recognize activities like Smoking, Washing Hands, Talking on the cell phone etc., We used curated YouTube video clips as training data to train this model. The use case for this model could be hand hygiene compliance monitoring in Hospitals and safety and privacy violations monitoring in Industries and work places.