In this article we will try to answer several questions: why is the near infrared band key for (camera-based) in-cabin monitoring systems to perform well in low light? Why is simulating the NIR a challenge? What solutions have been used so far to simulate it? How does Anyverse simulate it?
Why should you seriously consider synthetic data to train and validate in-cabin monitoring systems? What are the advantages of synthetic data versus real-world data to train these systems? And why are many DMS/OMS developers already implementing synthetic data in their data generation pipelines?
There are several in-cabin monitoring use cases, and likewise, they have different data needs, hence, different data challenges that you need to overcome if you want to successfully train the deep learning models behind the systems.
Whether it’s DMS, OMS, or any other interior camera system, acquiring data to develop in-cabin monitoring systems is challenging. But… Why is that? Why is acquiring real-world data particularly hard for the in-cabin monitoring use case?
DMS, OMS, HMI… We are heading 2022 and there is no doubt that interior sensing applications are rising exponentially, and they will continue to do so in the coming months.
The automotive interior sensing market was demanding a specific technology capable of successfully covering (in a cost & time-efficient manner) a wide range of potential issues when developing interior monitoring systems. Anyverse™ has responded and has brought a specific solution for interior simulation based on its top-performing synthetic data generation platform.