Our teams perform dense pixel semantic segmentation on millions of street images for various HiTech Autonomous Driving Applications. These image datasets train our client’s driverless car algorithms not just to ‘see’ but to interpret their environment and increase safety for passengers.

• We annotate objects like vehicles, pedestrians, etc. in an image or video used to enable object detection in autonomous vehicles
• We localize and extract number plate information from vehicular images using bounding box technique
• Signboards and traffic lights are crucial for autonomous vehicles. We annotate Signboards and traffic light images accurately using bounding boxes which are then used for training the models used in self driving cars.
• With polyline annotation, Wwe draw lines and splines over lanes & sidewalks for enabling lane differentiation in autonomous vehicles using polyline annotation using our tools.
• We train Facial Detection algorithms Autonomous vehicles on images captured by in-cabin cameras for monitoring driver’s movement and activities like a distraction, drowsiness, emotion, head/eye gaze, etc.
• Computer vision in Advanced Driver -Assisted Assistance Systems and Autonomous vehicles (ADAS) require handling of sensors like radar and LiDAR. We annotate sensor data, especially LiDAR data using various techniques. We also annotate pedestrians, road signs, cross-walkspedestrians, traffic dividing lines, and adjacent vehicles to enable computer vision in ADAS applications like collision warning, parking assistance, road sign detection, pedestrian detection stopping at traffic signals etc., collision warning and parking assistance.