Advanced driver-assistance systems (ADAS) equips cars and drivers with advanced information and technology to make them aware of the environment and handle potential situations better by semi-automation (SP Botkar. 2021). ADAS Annotation helps in training such applications to recognize the various objects and situations while taking quick and timely decisions automatically for safe driving.
Why ADAS for Controlled and Safe Driving?
Just like self-driving cars, ADAS also uses similar technology like radar, vision, and combinations of various sensors including LIDAR to automatize dynamic driving tasks like steering, braking, and acceleration of vehicles for controlled and safe driving.
To integrate these technologies, the ADAS needs labeled data in order to train the algorithm so that it can detect the various objects and body movements of the driver. Image annotation is one of the well-known services to create such training data for Computer Vision.
Is ADAS Different from Self-Driving Cars?
In self-driving cars or autonomous vehicles, the control is fully given to machine – like from driving to steering handling and braking, etc. There is no need for a driver, it can move in a predefined direction avoiding all types of objects without any human interference.
While in ADAS, all such assistance is installed to help or alert the drivers, when he/she is unable to recognize the situation. All the systems semi-autonomously work and take the required quick action in the absence of driver’s attention for safe and hustle-free driving.
ADAS Annotation for Object Detection
For ADAS object detection and human facial recognition or body movement detection, you need high-quality labeled data. Various types of image annotation techniques like bounding boxes, polygons and semantic segmentation are used to create such images.
Just like autonomous vehicles, ADAS enabled cars are also capable of analyzing sensory data by distinguishing the road from other objects like cars and pedestrians. We annotate all types of objects visible on roads including path lanes, street lights, signboards, other vehicles, pedestrians, lane signs, etc.
ADAS Annotation for Traffic Detection
We use the ground-truth labeling process to annotate recorded sensor data with an expected state of the automated driving system. For ADAS traffic labeling, it is using the right combination of Computer Vision techniques like pattern recognition, learning, feature extraction, tracking, 3D vision, etc.
Cogito is one of the well-known advanced driver assistance systems companies providing high-quality traffic detection data that will help you to develop a real-time algorithm capable of recognizing the traffic activity in future ADAS technology.
ADAS Annotation for Driver Monitoring
Drivers getting distracted, feeling tired, or drowsy can now be detected through the ADAS driving monitoring system. ADAS measures the indicators of the driver’s mental workload, behavior, and environment around the vehicle. Cogito is doing ADAS systems annotation via frames which will help ADAS to track the driver’s face, behavior and body movements.
ADAS Annotation for Facial Visual Analysis
Facial recognition software uses landmarks also known as nodal points techniques to detect faces. Cogito provides landmark and points annotation service to accurately measure the distances between the eyes, ears, mouth, and face of drivers. It has also introduced landmark annotation process for a 3D face shaped model to recognize the head pose variation, expression, and complex background.
Semantic Segmentation ADAS Annotation
Segmentation for ADAS is labeling and indexing an object in frames. In case they are more than one, each object is labeled in a unique color code without background noise. Eliminating background noise is necessary to ensure the quality of recognizing the edges of the object.
We cater to the needs of image semantic segmentation to recognize fixed and mandatory objects. Image segmentation is also created for Computer Vision applications from a low-level vision like 3D-reconstruction and motion estimation to solve high-level vision problems like image understating and scene parsing in CV.
Data labeling services for HD maps
Autonomous vehicles drive in a calibrated manner with perception, navigation, and control. High definition or HD maps allow these self-driving vehicles to move consciously in their ego-motion and according to the elements present in their environment. Comprising of road topology, road centerline geometry, and road-level attributes the High Definition or HD maps build real-time data processing, enabling vehicles to steer with control for autonomy on road. We provide data labeling services for HD maps and Lidar annotated data for a mesh of DNN and AI used in the autonomous driving mechanism around the world.
Why Cogito for ADAS Annotation and Data Labeling?
Working with well-known clients, Cogito is specialized in rendering high-quality image annotation services with the best quality and accuracy. For ADAS data labeling, we have a team of well-trained and experienced annotators creating accurately labeled data for testing and training the Computer Vision-based ADAS systems at negotiable pricing and scalable solution to meet your flexible needs.
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