IP Library Granted Patent US 12,425,739
Granted Patent B2
US 12,425,739 · App. 18/827,789 · Granted Sep 23, 2025

Camera digital gimbal system

Inventor: Peter Lablans (Morris Township, NJ)
H04N23/695H04N23/58H04N23/698
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,425,739
App. No.
18/827,789
Granted
Sep 23, 2025
Kind
B2
Abstract

A camera system has at least a first and a second camera which are attached to a common platform, each camera having a lens and an image sensor. Active areas of the image sensors of the first and second camera are determined. Image data only gathered from active areas of image sensors establish a panoramic image, which may be called an extended image space. Position sensors allow a processor to determine a location and a pose of the camera system. Coordinates of the extended image space are associated with a position of an object in physical space relative to a pointing direction of the camera system. A screen displays image data of a window of preset size around the image of the object in extended image space. A neural network is used to train the camera system to predict a position of an object based on camera rotations.

Claims (48)

1. A processor implemented method, comprising:

determining an extended image space of active areas of image sensors in at least a first camera and a second camera in a fixed position relative to the first camera in a common housing, active areas of image sensors of two or more cameras being determined by non-overlap of image areas generated by the image sensors of the at least first and second cameras, the extended image space representing a substantially registered image;

recording a sequence of video frames with the active areas of the image sensors of the at least first and second cameras;

collecting motion sensor data from one or more motion sensors associated with the common housing, wherein a movement of the common housing comprises at least one of a pitch, and a yaw;

determining coordinates of a point in extended image space with the common housing in a first pointing direction of the common housing and determining modified coordinates of the point in extended image space with the common housing in a second pointing direction of the common housing based on data generated by the one or more motion sensors;

capturing image data determined by a window being smaller than a size of the extended image space around the modified coordinates of the at least one point in the extended image space in the second position; and

displaying on the screen an image determined by the captured image data determined by the window.

2. The method of claim 1 , wherein the movement of the common housing comprises at least one of a pitch, a roll and a yaw.

3. The method of claim 2 , further comprising:

compensating the roll by scanline jumping based on the motion sensor data collected from the one or more motion sensors.

4. The method of claim 1 , further comprising:

determining the coordinates of the point associated with the first pointing direction based on a center of an object in the extended image space.

5. The method of claim 4 , further comprising:

tracking the object in the extended image space in extended image space by an object tracker and determining the modified coordinates based on coordinates of the tracked object in extended image space with the common housing in the second position, wherein a center of the tracked object is a center of an e-gimbal window.

6. The method of claim 4 , further comprising:

training a convolutional neural network to perform object tracking in the extended image space.

7. The method of claim 1 , wherein the first and second cameras are identical and have a curved image sensor.

8. The method of claim 1 , wherein the first and second cameras are self-aligning.

9. The method of claim 1 , further comprising:

displaying the captured image data at a frame rate of at least 10 frames per second.

10. The method of claim 1 , further comprising:

associating with a neural network data generated by the one or more motion sensors with modified coordinates in the extended image space.

11. The method of claim 1 , further comprising:

predicting by a neural network one or more rotation corrective instructions based on one or more inputs from the positional sensors.

12. A camera system, comprising:

one or more storage devices enabled to store data, including instructions;

a screen to display an image;

one or more processors, enabled to retrieve instructions from the one or more storage devices, the instructions when executed by the one or more processors cause to carry out computer functions, comprising:

determining an extended image space of active areas of image sensors in at least a first camera and a second camera in a fixed position relative to the first camera in a common housing, active areas of image sensors of two or more cameras being determined by non-overlap of image areas generated by the image sensors of the at least first and second cameras, the extended image space representing a substantially registered image;

recording a sequence of video frames with the active areas of the image sensors of the at least first and second cameras;

collecting motion sensor data from one or more motion sensors associated with the common housing, wherein a movement of the common housing comprises at least one of a pitch, and a yaw;

determining coordinates of a point in extended image space with the common housing in a first pointing direction of the common housing and determining modified coordinates of the point in extended image space with the common housing in a second pointing direction of the common housing based on data generated by the one or more motion sensors;

capturing image data determined by a window being smaller than a size of the extended image space around the modified coordinates of the at least one point in the extended image space in the second position; and

displaying on the screen an image determined by the captured image data determined by the window.

13. The camera system of claim 12 , wherein the movement of the common housing comprises at least one of a pitch, a roll and a yaw.

14. The camera system of claim 13 , further comprising to carry out the computer function:

compensating the roll by scanline jumping based on the motion sensor data collected from the one or more motion sensors.

15. The camera system of claim 12 , further comprising:

determining the coordinates of the point associated with the first pointing direction based on a center of an object in the extended image space.

16. The camera system of claim 15 , further comprising:

tracking the object in the extended image space by an object tracker and determining the modified coordinates based on coordinates of the tracked abject in extended image space with the common housing in the second position.

17. The camera system of claim 15 , further comprising:

training a convolutional neural network to perform object tracking in the extended image space.

18. The camera system of claim 12 , wherein the first and second cameras are identical and have a curved image sensor.

19. The camera system of claim 12 , further comprising:

associating with a neural network data generated by the one or more motion sensors with modified coordinates in the extended image space.

20. The camera system of claim 12 , further comprising:

predicting by a neural network one or more rotation corrective instructions based on one or more inputs from the positional sensors.

Continuity (3)
Continuation In Part 17866525 · Jul 17, 2022
Continuation In Part 17037228 · Sep 29, 2020
Related Publication 20240430573A1 · Dec 26, 2024
References Cited (99)
US 5947359A · Yoshie · 1999 [cited by applicant]
US 6900837B2 · Muramatsu et al. · 2005 [cited by applicant]
US 6972796B2 · Katta · 2005 [cited by examiner]
US 7161618B1 · Niikawa et al. · 2007 [cited by applicant]
US 7209176B2 · Chapman · 2007 [cited by applicant]
US 7355305B2 · Nakamura et al. · 2008 [cited by applicant]
US 7418275B2 · Yiu · 2008 [cited by applicant]
US 7495694B2 · Cutler · 2009 [cited by examiner]
US 7616248B2 · Parulski et al. · 2009 [cited by applicant]
US 7667765B2 · Turley et al. · 2010 [cited by applicant]
US 7676150B2 · Nakashima · 2010 [cited by applicant]
US 7688203B2 · Rockefeller et al. · 2010 [cited by applicant]
US 7688306B2 · Wehrenberg et al. · 2010 [cited by applicant]
US 7734254B2 · Frost et al. · 2010 [cited by applicant]
US 7741961B1 · Raffi et al. · 2010 [cited by applicant]
US 7835736B2 · Larocca · 2010 [cited by applicant]
US 8180410B2 · Kim · 2012 [cited by applicant]
US 8355042B2 · Lablans · 2013 [cited by applicant]
US 8416282B2 · Lablans · 2013 [cited by examiner]
US 8896697B2 · Golan et al. · 2014 [cited by applicant]
US 9041898B1 · Reilly et al. · 2015 [cited by applicant]
US 9874308B2 · Saika et al. · 2018 [cited by applicant]
US 9939608B2 · Peng et al. · 2018 [cited by applicant]
US 10033303B2 · Liu et al. · 2018 [cited by applicant]
US 10095942B2 · Mentese et al. · 2018 [cited by applicant]
US 10104286B1 · Yu · 2018 [cited by examiner]
US 10203515B2 · Liu et al. · 2019 [cited by applicant]
US 10205879B2 · Segapelli · 2019 [cited by applicant]
US 10274129B2 · Saika · 2019 [cited by applicant]
US 10277821B2 · Shigeoka · 2019 [cited by applicant]
US 10277858B2 · Kankaanpaa et al. · 2019 [cited by applicant]
US 10281737B2 · Park et al. · 2019 [cited by applicant]
US 10284782B2 · Yamazaki · 2019 [cited by applicant]
US 10354407B2 · Lablans · 2019 [cited by applicant]
US 10585344B1 · Lablans · 2020 [cited by applicant]
US 10616503B2 · Lu · 2020 [cited by examiner]
US 10795006B2 · Garcia et al. · 2020 [cited by applicant]
US 10819912B2 · Jin · 2020 [cited by examiner]
US 10831093B1 · Lablans · 2020 [cited by applicant]
US 10896327B1 · Lablans · 2021 [cited by applicant]
US 11049218B2 · Khwaja · 2021 [cited by examiner]
US 11119396B1 · Lablans · 2021 [cited by applicant]
US 11470249B1 · Gupta · 2022 [cited by examiner]
US 11848349B1 · Keefe et al. · 2023 [cited by applicant]
US 20040252759A1 · John Winder · 2004 [cited by examiner]
US 20070254640A1 · Bliss · 2007 [cited by applicant]
US 20090086015A1 · Larsen et al. · 2009 [cited by applicant]
US 20090153686A1 · Huang · 2009 [cited by applicant]
US 20100097442A1 · Lablans · 2010 [cited by examiner]
US 20100214398A1 · Goulart et al. · 2010 [cited by applicant]
US 20100245585A1 · Fisher et al. · 2010 [cited by applicant]
US 20110058052A1 · Bolton et al. · 2011 [cited by applicant]
US 20110205340A1 · Garcia et al. · 2011 [cited by applicant]
US 20170302852A1 · Lam · 2017 [cited by applicant]
US 20180027180A1 · Baldwin · 2018 [cited by examiner]
US 20190011932A1 · McGrath · 2019 [cited by applicant]
US 20190066305A1 · Liao · 2019 [cited by examiner]
US 20190082110A1 · Jin · 2019 [cited by examiner]
US 20190082114A1 · Jeon · 2019 [cited by examiner]
US 20190180468A1 · Hildreth · 2019 [cited by applicant]
US 20200020075A1 · Khwaja · 2020 [cited by examiner]
US 20200077023A1 · Kang et al. · 2020 [cited by applicant]
US 20200382702A1 · Oberdoerster · 2020 [cited by examiner]
US 20240040250A1 · Shi et al. · 2024 [cited by applicant]
CN 112887554 · 2021 [cited by applicant]
GB 10217362 · 2010 [cited by applicant]
Wikipedia, Image stabilization, online webpage at https://en.wikipedia.org/w/index.php?title=Image_stabilization&oldid=892981277, last edited on Apr. 18, 2019. [cited by applicant]
Niftychic, Micro Security Camera, downloaded from https://niftychic.net/products/micro-security-camera?variant=8855162224701&gclid=EAlalQobChMlvObs6t_74QIVkozlCh3ekA5JEAQYBCABEgJRIPD_BWE. [cited by applicant]
Jim Morrison et al., Apple iphone SE Teardown, downloaded May 15, 2019 from http://www.chipworks.com/ko/node/359. [cited by applicant]
Omnivision, OVM9724 CameraCubeChip, Product Brief, downloaded from https://www.ovt.com/download/sensorpdf/144/OmniVision_OVM9724.pdf. [cited by applicant]
Naneye/Naneye Stereo, Miniature Camera Module, Datasheet DS000501, 2018-)ct-05 dowloaded from https://ams.com/documents/20143/36005/NanEye_NanEyeStereo_DS000501_2-02.pdf/f46c15da-52fb-78fd-aea6-faf4cf784da2. [cited by applicant]
Dr. Ing. Janocha, Saarland University, Microactuators—Principles, Applications, Trends, dowloaded May 14, 2019 from http://www.lpa.uni-saarland.de/pdf/a2-1.pdf. [cited by applicant]
Squiggle Micro Motor Technology, Inside the M3 Module, dowloaded on May 21, 2019 from https://www.newscaletech.com/resources/technology/squiggle-micro-motor-technology/. [cited by applicant]
Adam Ciecko et al., Analysis of the Accuracy and Usefulness of MEMS Chipsets Embedded in Popular Mobile Phones in Inertial Navigation, I 2019 IOP Conf. Ser.: Earth Environ. Sci. 221 012070. [cited by applicant]
La Rosa et al., Optical Image Stabilization (OIS), STMicroelectronics, downloaded from https://www.st.com/content/ccc/resource/technical/document/white_paper/c9/a6/fd/e4/e6/4e/48/60/ois_white_paper.pdf/files/ois_white_p… [cited by applicant]
Dahary et al., Digital Gimbal: End-to-end Deep Image Stabilization with Learnable Exposure Times, 2021, downloaded from https://arxiv.org/pdf/2012.04515.pdf. [cited by applicant]
Souza et al., Digital video stabilization based on adaptive camera trajectory smoothing; Souza and Pedrini EURASIP Journal on Image and Video Processing (2018) 2018:37 https://doi.org/10.1186/s13640-018-0277-7. [cited by applicant]
Souza et al., Digital Video Stabilization: Algorithms and Evaluation; downloaded from https://sol.sbc.org.br/index.php/ctd/article/download/6338/6235/. [cited by applicant]
Guilluy et al., Video stabilization: overview, challenges and perspectives, 2021 downloaded from https://www.sciencedirect.com/science/article/pii/S0923596520301697. [cited by applicant]
Guenter et al. Highly curved image sensors: a practical approach for improved optical performance, https://doi.org/10.1364/OE.25.013010. [cited by applicant]
Sigurd Ljodal Master's Thesis 2014, Implementation of a real-time distributed video processing pipeline, downloaded from https://core.ac.uk/download/pdf/30903173.pdf. [cited by applicant]
Espen Oldeide Helgedagsrud in Master's Thesis Efficient implementation and processing of a real-time panorama video pipeline with emphasis on dynamic stitching; downloaded from https://www.duo.uio.no/bitstream/handle/10… [cited by applicant]
Bagadus: An Integrated Real-Time System for Soccer Analytics, Hakon Kvale Stensland et al. 2014, ACM Transactions on Multimedia Computing, Communications and Applications, vol. 10, No. 1s, Article 14, Publication date: … [cited by applicant]
Di Febbo et al., Real-Time Image Distortion Correction: Analysis and Evaluation of FPGA-Compatible Algorithms, 2916 downloaded from https://arxiv.org/pdf/1610.09712.pdf. [cited by applicant]
Van der Jeught S, Buytaert JN, Dirckx JJ; Real-time geometric lens distortion correction using a graphics processing unit. Opt. Eng. 0001;51(2):027002-1-027002-5. doi:10.1117/1.OE.51.2.027002. [cited by applicant]
Thinh Huynh, A Study on Motion Control of Gimbal-based Target Tracking System, Feb. 2022, Pukyong National University, downloaded from https://repository.pknu.ac.kr:8443/bitstream/2021.oak/24416/2/A%20Study%20on%20Motio… [cited by applicant]
Lee et al, 3D Video Stabilization with Depth Estimation by CNN-based Optimization, 2021, downloaded from https://openaccess.thecvf.com/content/CVPR2021/papers/Lee_3D_Video_Stabilization_With_Depth_Estimation_by_CNN-Base… [cited by applicant]
VIDIO from MOSAIK Studio, Inc. of Orlando FL downloaded from www.vidio.ai. [cited by applicant]
Hagui et al. A Comparison of OpenCV Algorithms for Human Tracking with a Moving Perspective Camera, EUVIP2021—9th European Workshop on Visual Information Processing, Jun. 2021, Paris (virtuel), France. ff10.1109/EUVIP50… [cited by applicant]
OpenCV Camera Calibration downloaded from in https://docs.opencv.org/4.x/dc/dbb/tutorial_py_calibration.html w. [cited by applicant]
Camera Matrix Lecture downloaded from https://www.cs.cmu.edu/˜16385/s17/Slides/11.1_Camera_matrix.pdf. [cited by applicant]
Lecture 12: Camera Projection published by Penn State University at https://www.cse.psu.edu/˜rtc12/CSE486/lecture12.pdf. [cited by applicant]
Computer Vision with Tensowflow downloaded from https://www.tensorflow.org/tutorials/images. [cited by applicant]
Bogdan et al. Calibration of Wide Field-of-View Cameras, CVMP '18, Dec. 13-14, 2018, London, United Kingdom, downloaded from https://doi.org/10.1145/3278471.3278479. [cited by applicant]
Bogdan, DeepCalib Software downloaded from https://github.com/alexvbogdan/DeepCalib. [cited by applicant]
Shavit et al. Introduction to Camera Pose Estimation, downloaded from https://arxiv.org/pdf/1907.05272. [cited by applicant]
Victor Zhou CNNs, Part 1: Training a Convolutional Neural Network, downloaded from https://victorzhou.com/blog/intro-to-cnns-part-1/. [cited by applicant]
Victor Zhou CNNs, Part 2: Training a Convolutional Neural Network, downloaded from https://victorzhou.com/blog/intro-to-cnns-part-2/. [cited by applicant]
Stephanie Doyle AI 101, 2023 downloaded from https://www.backblaze.com/blog/ai-101-gpu-vs-tpu-vs-npu/. [cited by applicant]