Time of Flight Camera Region Detection for Depth Computation
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Solution Overview
Problem
Time-of-flight cameras face challenges in efficiently computing depth from raw sensor data due to resource intensity and noise, particularly in distinguishing regions of interest like hands from background surfaces, especially in complex hand poses or interactions.
Innovation Solution
A new processing pipeline that includes a region detector trained to learn associations between image elements and regions of interest, using a classifier like a random decision forest to segment and detect candidate regions, thereby allocating resources efficiently and computing depth only for these regions, rather than the entire frame.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If depth is computed from the entire frame, then complete depth coverage is achieved, but computational resource usage increases
Solution Approach 1:
The patent segments the image frame into multiple regions of interest using a region detector that identifies areas containing objects of interest (such as hands). Depth computation is then performed only within these segmented regions rather than the entire frame, reducing computational resource usage while maintaining depth accuracy for relevant areas.
2Productivity
If region detection is performed to identify objects of interest, then depth computation efficiency improves, but system complexity increases
Solution Approach 1:
The patent applies preliminary action by performing region detection before depth computation. The region detector pre-identifies areas containing objects of interest, and this preliminary segmentation is then used to guide the depth computation process, improving overall efficiency despite adding a preprocessing step.
3Use of energy by moving object
If depth computation is performed only on regions of interest, then resource usage is reduced, but measurement coverage decreases
Solution Approach 1:
The patent applies local quality by providing different processing treatments to different regions of the image. Regions of interest receive full depth computation processing, while background regions receive reduced or no processing. This ensures high measurement precision where needed while conserving computational resources in less critical areas.
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 approach reduces computational resource usage and improves accuracy in detecting regions of interest and computing depth, especially in noisy and complex scenes, enabling efficient and precise depth mapping for applications like human-computer interaction and automotive systems.
Implementation Method 1
Time-of-flight (TOF) cameras are increasingly used in a variety of applications... The camera receives the reflected light that, dependent on the distance of an object to the camera, experiences a delay. Given the fact that the speed of light is known, a depth map may be generated.
Data Source
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AI summary
Region of interest detection in raw time of flight images is described. For example, a computing device receives at least one raw image captured for a single frame by a time of flight camera. The raw image depicts one or more objects in an environment of the time of flight camera (such as human hands, bodies or any other objects). The raw image is input to a trained region detector and in response one or more regions of interest in the raw image are received. A received region of interest comprises image elements of the raw image which are predicted to depict at least part of one of the objects. A depth computation logic computes depth from the one or more regions of interest of the raw image.