We understand the enormous potential of Computer Vision technology and have utilized it to read emotions, gestures, facial expressions, eyeball movement, language use…
Furthering our effort to transform and simplify the world around us, we have developed an advanced Deep Learning based product that has the capability to detect roads, buildings, trees etc.
We need to pay close attention while driving a car and any kind of distraction can cost us heavily. If by any chance the car driver takes his attention away from the road due to his eyes, hands…
Detect the gender and ethnicity of the detected face. Not only this but you can estimate the age of the person. Extend it with other models for comparison and classification.
Detect the vehicles, pedestrians, and road signs in a traffic scenic image or video from the stream of data. This model provides the scalability to integrate with other neural networks.
Experience the real time detection and segmentation of the people in the image or video without any restriction on the movement of people. Highly integration capability with other APIs
Detect the faces in the image or video even during low light conditions, few scenarios could be like snippets from night or driver cabins and not only this but can be detected from IR cameras.
LPR serves as Optical Character Recognition (OCR), detecting the vehicle and its license plate to extract the license plate number of the vehicles in the move over a time.
Locate a person or multiple people in the video feed irrespective of people's positions whether they are standing, sitting, walking etc., in a real time video stream.
Detecting the faces of the people can't be any easier than just with an image or a video. This can be the input to the face identification network even with minimum 30% of face visibility.
Localize human faces and identify 68 key landmarks from an image or video. This methodology plays a key role in understanding the facial features of the person.
Estimate the heart rate in beats per minute by detecting a face from a simple RGB channel enabled by the camera, It doesn’t require any special hardware sensors.
Track the pose and orientation of a person from an image or video by estimating the spatial positions of body joints such as the elbow, wrists, ankle, shoulder and knees etc.,