Predictive Camera Exposure Adjustment for AR Tracking
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Solution Overview
Problem
Conventional camera systems in augmented reality (AR) and tracking systems face challenges in adjusting exposure settings quickly enough to maintain accurate feature detection when moving between significantly different light levels, leading to temporary loss of image-based feature detection and drift in camera positioning.
Innovation Solution
A predictive system that determines the direction and future location of a camera assembly, allowing for pre-adjustment of exposure settings based on predicted environmental factors, such as light levels, to capture images without delay in auto-adjustment, thereby reducing lost frames and feature tracking gaps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional camera systems use automatic exposure adjustment, then the camera can adapt to different lighting conditions, but the adjustment delay causes loss of image frames and feature tracking gaps when moving between significantly different light levels
Solution Approach 1:
The system performs preliminary action by predicting future light levels based on camera movement direction and pre-adjusting exposure settings before the camera actually enters the new lighting condition. This predictive pre-adjustment eliminates the conventional delay where the camera reacts after entering a new light level, thereby preventing frame loss and feature tracking gaps during transitions between significantly different lighting environments.
2Reliability
If the camera uses fixed exposure settings, then the system operates without adjustment delay, but feature detection accuracy deteriorates when moving between different light levels
Solution Approach 1:
The system implements dynamics by transitioning from fixed exposure settings to dynamically adjusted exposure settings that adapt in real-time to changing lighting conditions. The exposure settings are continuously modified based on predicted future light levels derived from camera movement analysis, ensuring that feature detection accuracy is maintained across varying illumination environments without interruption or loss of tracking.
3Speed
If the camera system increases the speed of exposure adjustment, then response time to lighting changes improves, but the complexity of the auto-adjustment system increases
Solution Approach 1:
The system introduces an intermediary predictive model that acts as a mediator between camera movement data and exposure settings. Instead of directly and complexly analyzing lighting conditions and adjusting settings in real-time, the system uses the predictive model to estimate future light levels based on current camera movement direction, thereby simplifying the control architecture while achieving fast response times without excessive system complexity.
Data Source
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AI summary
Systems, devices, methods, computer-readable storage media, and electronic apparatuses for camera setting adjustment based on predicted environmental factors are provided. An example system includes a camera assembly, at least one processor, and memory storing instructions. When executed by the at least one processor, the instructions may cause the system to determine a direction of movement of the camera assembly. The instructions may also cause the system to predict environmental factors based on the direction of movement. Additionally, the instructions may cause the system to determine camera settings based on the environmental factors. The instructions may also capture an image with the camera assembly using the determined camera settings. In some implementations, the captured image is used to track or one or more entities.