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The right synthetic data makes all the difference in advanced perception and machine learning. It can fill in data gaps or complement real-world footage in a variety of industries. At Anyverse™ we can simulate any scenario and cover a whole range of corner cases with accuracy that consequently boosts your AI.
Anyverse™ supports a wide range of applications for autonomous vehicles and drive-assist development. Firstly, we can model any scenario using a geographically stylized urban, suburban, rural and highway environments. Secondly, we can randomly add high-quality vehicles and pedestrians to follow specific behaviors.
Classification
and detection of vehicles and pedestrians
Corner cases (i.e. pedestrians at night)
Classification and detection of traffic lights
Detection of empty spaces in parking lots
The power of deep learning AI to automatically detect different types of events in traffic and people’s behavior through smart cameras. The amount of data to train the deep learning model can bee overwhelming and cities face privacy regulations and to use real-world data. Moreover, real-world data may lack enough variability to avoid bias in the systems and most probably won’t include corner cases to avoid false negatives.
Urban/suburban
surveillance
Traffic control
Incidents/events detection
People flow/access control
Unmanned Aerial Vehicles (UAV) are widely used across different industries. Anyverse™ supports drones as a kind of an ego-vehicle, with an arbitrary number of cameras in defined 3D fly-thru scenarios. Moreover, we can add ground truth data for objects of interest or defective parts to the custom scenes.
Package delivery in urban/suburban areas
Infrastructure
inspection
Accidents in streets or highways
Airport-related security operations
Synthetic data may prove useful for training smart cameras inside vehicles or other indoor scenarios. Anyverse™ can, for example, add a rich database of 3D people to these scenarios. Variability then applies to lighting, objects, textures, poses and behaviors.
Warning distracted or drowsy drivers
Detecting serious issues such as a child removing their seat belt
Detecting certain behaviors or dangerous situations in home environments
We’d love to know more.