In order to navigate obstacles and operate effectively in chaotic real world traffic conditions, computer vision based models must be trained with annotated data that adds information and labels to images and video. We Assist you to prepare a wide range of datasets for training and validating autonomous vehicles
Classify and detect all vehicle,pedestrians,etc in the image. Assign a class to each object and Annotate with various technique to prepare training datasets for Autonomous Vehicles.
We annotate Traffic signal Lights,sighn boards,Lamp poles,etc using Bounding box,polygon Techniques and attribute current traffic light color status.
We annotate various obstacles in road like animals, fallen trees, electric posts etc., to train computer models for detecting obstacles and avoid collisions and accidents.
Lane Line detection is a critical component for self driving cars and also for computer vision in general. This concept is used to describe the path for self-driving cars and to avoid the risk of getting in another lane.
Vehicle Number Plate Detection aims at detection of the License Plate Using Bounding Box technique present on a Vehicle and then extracting the contents of that License Plate.
We annotate various objects in cloud point data acquired by LiDAR and Radar sensors used in autonomous vehicles.
In-cabin monitoring systems detect each vehicle occupant's unique ability to safely multi-task while in the vehicle, creating more safety on the road.Mainly Drivers movements and activities.
Object tracking in Autonomous , Detects all objects like car,pedestrians,etc and develops a unique identification for each of the initial detections and then tracks the detected objects as they move around frames in a video.
