Faro Technologies. has filed a patent for a computer-implemented method involving machine learning models to detect objects in a video stream from a camera. If the confidence score is below a threshold, the method determines a direction to move the camera for better detection. GlobalData’s report on Faro Technologies gives a 360-degree view of the company including its patenting strategy. Buy the report here.
According to GlobalData’s company profile on Faro Technologies, Lidar-based object detection was a key innovation area identified from patents. Faro Technologies's grant share as of January 2024 was 51%. Grant share is based on the ratio of number of grants to total number of patents.
Camera system using machine learning to track objects
The patent application (Publication Number: US20240031680A1) describes a method and system for enhancing object detection in a video stream captured by a camera. The method involves receiving the video stream, detecting an object of interest using machine learning models, and determining a direction to move the camera if the confidence score associated with the object does not meet a threshold. This direction is then presented as an indication to the user. The system includes a camera with a wide field of view and a processing system to execute the object detection and camera movement instructions.
The camera system described in the patent utilizes a 360-degree image acquisition system with overlapping fields of view from multiple photosensitive arrays. The method involves training machine learning models to detect objects and determine optimal camera movement directions based on the distance between the center point of the camera's field of view and the object's bounding box centroid. The system also incorporates support vector trees and Gaussian measure trees in the machine learning models to enhance object detection accuracy. Additionally, a field programmable gate array is used to execute the object detection and camera movement instructions efficiently. Overall, the patent application outlines a comprehensive approach to improving object detection in video streams by dynamically adjusting the camera's position based on real-time analysis of the detected objects.
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