In-Cabin 3D Object Classification Using Reflectivity Maps
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current driver assistance systems lack the capability to accurately and seamlessly transition from autonomous to manual mode, requiring improved classification of objects and recognition of driver gestures within the vehicle interior for enhanced safety and user experience.
Innovation Solution
A method and device utilizing a 3D image capture unit and processing unit to generate a reflectivity map from 2D intensity and 3D depth images, enabling precise classification of objects and recognition of gestures by calculating reflectivity, particularly effective in the near-infrared range, using time-of-flight cameras and optical bandpass filters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If object classification is improved for better driver monitoring, then safety and gesture recognition are enhanced, but system complexity and computational requirements increase
Solution Approach 1:
The system segments the classification process by first identifying skin regions based on color information, then separately analyzing gesture patterns within those regions. This division allows complex gesture recognition to be broken down into manageable steps: skin detection → region extraction → gesture classification, reducing overall system complexity while maintaining high accuracy
Solution Approach 2:
The system transitions from analyzing only intensity images to incorporating color space information (HSV color model) as an additional dimension. By adding color as a classification dimension, the system achieves more accurate skin detection and gesture recognition without significantly increasing hardware complexity, as color processing can be performed through software algorithms
2Measurement precision
If 3D depth information is utilized for better object classification, then classification accuracy is improved, but data processing requirements and computational load increase
Solution Approach 1:
The system applies local quality by focusing computational resources only on skin-detected regions rather than processing the entire image. Once skin regions are identified using color information, the system concentrates depth analysis and gesture recognition computations specifically within those localized areas, significantly reducing overall computational load while maintaining high classification accuracy
Solution Approach 2:
The system performs preliminary skin detection using color information before conducting detailed 3D depth analysis. This preliminary filtering step identifies only the relevant regions that require intensive processing, allowing the system to prepare and prioritize computational resources in advance, thereby reducing overall processing requirements
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This solution provides accurate and reliable classification of objects and gesture recognition, enhancing the transition between autonomous and manual driving modes, improving safety and user experience through precise monitoring of the driver's state and interactions.
Implementation Method 1
Each of the 3D images includes at least one 2D intensity image and at least one 3D depth map of the scene
Implementation Method 2
The illumination unit preferably operates in the near infrared (NIR) range
Implementation Method 3
The 3D image capture unit can, for example, comprise an optical bandpass filter that only transmits the wavelength used by the illumination, thereby eliminating a large portion of the interfering background light
Data Source
Figure 1
Figure 2
Figure 3
AI summary
The invention relates to a method for classifying objects (2) within the interior (3) of a vehicle (1), having the following steps: providing at least one 3D image-capturing unit (4) which is designed to capture 3D images of a scene, providing at least one processing unit (5) which is coupled to the 3D image-capturing unit (4), said processing unit (5) being designed to receive 3D images from the 3D image-capturing unit (4); detecting 3D images of a scene, each of the 3D images comprising at least one 2D intensity image and at least one 3D depth map of the scene; calculating a reflectivity map from the at least one 2D intensity image and the at least one 3D depth map using the processing unit (5); and classifying at least one object (2) of the scene using the reflectivity of the object. The invention additionally relates to a device for classifying objects (2) within the interior (3) of the vehicle (1), comprising a 3D image-capturing unit (4) and a processing unit (5) coupled to the 3D image-capturing unit (4) for carrying out the method. The invention additionally relates to a driver assistance system comprising a device for carrying out the method and to a computer program product comprising commands which prompt a computer to carry out the method when the program is ran by said computer.