NVIDIA Corporation (20240312187). FEATURE TRACKING FOR AUTONOMOUS SYSTEMS AND APPLICATIONS simplified abstract
Contents
FEATURE TRACKING FOR AUTONOMOUS SYSTEMS AND APPLICATIONS
Organization Name
Inventor(s)
Yue Wu of Mountain View CA (US)
Cheng-Chieh Yang of Sunnyvale CA (US)
Xin Tong of Santa Clara CA (US)
Minwoo Park of Saratoga CA (US)
FEATURE TRACKING FOR AUTONOMOUS SYSTEMS AND APPLICATIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240312187 titled 'FEATURE TRACKING FOR AUTONOMOUS SYSTEMS AND APPLICATIONS
Simplified Explanation: The patent application describes systems and methods for feature tracking in autonomous or semi-autonomous systems by merging features detected using feature trackers and feature detectors to track features between images.
Key Features and Innovation:
- Merge features detected by trackers and detectors for improved tracking.
- Limit the number and locations of merged features to focus on important tracking.
- Prioritize tracking features associated with objects near the driving surface in autonomous driving applications.
Potential Applications: The technology can be applied in autonomous driving systems, surveillance systems, robotics, and augmented reality applications.
Problems Solved: The technology addresses the challenge of efficiently tracking features in dynamic environments while prioritizing important features for tracking.
Benefits:
- Enhanced feature tracking accuracy.
- Improved performance in autonomous systems.
- Efficient utilization of computational resources.
Commercial Applications: The technology can be utilized in autonomous vehicle navigation systems, security surveillance systems, industrial automation, and virtual reality applications, potentially revolutionizing these industries.
Prior Art: Readers can explore prior research in feature tracking, computer vision, and autonomous systems to understand the evolution of similar technologies.
Frequently Updated Research: Stay updated on advancements in feature tracking algorithms, computer vision techniques, and applications in autonomous systems for the latest developments in the field.
Questions about Feature Tracking: 1. How does feature tracking contribute to the efficiency of autonomous systems? 2. What are the key differences between feature trackers and feature detectors in image processing?
Original Abstract Submitted
in various examples, feature tracking for autonomous or semi-autonomous systems and applications is described herein. systems and methods are disclosed that merge, using one or more processes, features detected using a feature tracker(s) and features detected using a feature detector(s) in order to track features between images. in some examples, the number of merged features and/or the locations of the merged features within the images are limited. this way, the systems and methods are able to identify merged features that are of greater importance for tracking while refraining from tracking merged features that are of less importance. for example, if the systems and methods are being used to identify features for autonomous driving, a greater number of merged features that are associated with objects located proximate to the driving surface may be tracked as compared to merged features that are associated with the sky.
