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 our computer vision algorithm that calculates the joint angles of a person and calculates their ergonomics risk score. This is called as RULA (Rapid Upper Limb Assessment) score. This can be used for posture analysis and injury prevention
Personal Protective Equipment (PPE) like vest, helmet and goggles are mandatory in certain hazardous work places. We built a vision intelligence based AI model that can monitor PPE compliance by employees. Our team developed this model using the latest advances in training Machine Learning model using Neural Networks. We optimized this model to run in inexpensive embedded systems. We connect a camera to this device and run our model to track the PPE compliance. Thus this camera becomes a smart camera capable of detecting PPE. This device can be installed in entrances to monitor adherence of PPE when employee enter the hazardous areas. We can easily configure this device to send notifications of any violations. Our team designed this device with employee privacy in our minds. The device doesn’t record or transmit video frame. In fact, this device works in stand-alone mode and doesn’t have to be connected to any network.
A demo of using Caffe based Person detector along with distance from camera. Tested in different moving platforms like Fork-lift, car etc., Achieved detection at more than 50 ft. The who setup is running in a Rasberry Pi with an attached Movidius NCS
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.
This is a Convolutional Neural Network (CNN) trained to detect Personal Protective Equipment (PPE) compliance in an industrial setting. This model is trained using the MobileNet SSD Neural Network. This is part of our work to build smaller, faster Computer Vision models to perform real time pedestrian and object detect in different settings in the Safety, Surveillance and Compliance space in any industry. Our goal is to build these models that are small enough to run on the edge i.e in small devices like a drone.
Real time person detection in using Intel Movidius Neural Compute Stick (NCS) attached to a Rasberry. The NCS does all the inference and Rasberry Pi is the host. We are running MobileNet SSD running in Caffe framework trained to detect persons. We are working to integrate this with Vehicle Safety systems and install on vehicles like Forklift to offer automated collision avoidance systems that improves industrial safety.
A demo of using our Machine Learning Computer Vision detector to detect PPE (Personal Protective Equipment) that include safety vest and helmet in an industrial setting.