# Gesture-Based User Interface for Vehicle On-Board System: A Questionnaire and Research Approach
## 3 Proposed Approach of Gesture-Based Interaction Interface
### Gesture Acquisition  
As presented in the \"Related works\" section gestures can be acquired using different technologies. Wide use of devices such as smartwatches, equipped with additional sensors (such as accelerometers or gyroscopes), can significantly facilitate the gesture recognition. There are also other wireless technologies (e.g., RFID), but we focus our attention on computer vision, emphasizing its non-invasive nature.  
The interior of the car is a difficult environment because the lighting conditions are very different [6, 35]. To recognize the actions performed by a driver the scene appearance should be acquired first. Typical capture devices, i.e., cameras that operate in the visible spectrum (electromagnetic waves from about 380 to 780 nm of wavelengths), can work properly with good lighting conditions. Despite the fact that often the parameters of such cameras are very high, in terms of resolution, sensitivity and dynamic range, They suffer from poor lighting condition encountered in road conditions. It is obvious that the driver's face while driving cannot be illuminated with any sort of additional light source and for this reason other acquisition mechanisms are employed. Insensitive to the light source, its power or type is thermal imagery technique. It registers the temperature of objects giving the thermal image of the scene. Despite the fact that this technique can be successfully used in human-computer interfaces (e.g., [36]) the price of thermal cameras is still a serious barrier.  
In view of the above, the most appropriate solution seems to be the depth map technique. Here, the distance information from the scene objects to the camera is represented as pixels brightness. There are three major approaches to acquire depth information: stereo vision, structured-light, and time-of-flight. The first requires a stereoscopic camera or a pair of calibrated cameras. Due to the use of regular cameras, this approach is not suitable for use in a vehicle. In the structured light approach, an infrared (invisible for the human eye) light pattern is projected on the scene. Distorted on 3-D objects, it is registered by the IR camera offering, after geometric reconstruction, the 3D information about the observed scene. The time-of-flight approach is based on the measurements of the time required for a light signal to travel from the source to the scene and back to the sensor.  
For our study we have employed the Intel RealSense sr300 camera (presented in Figure 2). The considered alternatives to this solution are MS Kinect and Leap Motion. The first and the second generation of Kinect hardware was excluded due to its dimensions. What is more, both versions are now considered obsolete and are not manufactured anymore. The latest release of Kinect (the Azure Kinect released this-2020-year) strictly base its operation on cloud computing, which exclude its usage on sparsely populated areas outside network coverage. The Leap Motion could have been a good alternative here for its small size and quite decent operation. However, the solution offers a limited number of movement patterns recognition (i.e., swipe, circle, key tap and screen tap).  
In view of the above, the Intel RealSense provides the best suited solution for our research. It has a compact size suitable for the in-car use. The sensor includes both infrared and RGB colour cameras, IR laser projector and a microphone array. It captures a depth image of the scene in good and poor lighting conditions from a relatively close distances occurring in the car cabin. It offers 60 fps in 720 p resolution and 30 fps in 1080 p. The hardware load from today's perspective also seems to be undemanding. The sensor requires at least 6 th Generation Intel(r) CoreTM Processor and 4 GB of memory. With a price slightly exceeding $100 the component of this kind seems to be well suited for the widespread use.  
We performed all experiments on the laptop equipped with 8 th generation Intel(r) CoreTM i5 Processor, 8 GB of memory and observed no delays during the operation.