IP Library › Granted Patent US 12,425,741
Granted Patent B2
US 12,425,741 · App. 17/951,961 · Granted Sep 23, 2025

Systems and methods for motion detection, quantification, and/or measurement with exposure correction in video-based time-series signals

Inventors: Timothy Shields (Beverly, MA); William D. Marscher (Morristown, NJ); Sergey Frolov (New Providence, NJ)
Assignee: Mechanical Solutions Inc.
H04N23/73G06T5/10G06T5/73G06T7/20G06T7/80H04N5/91H04N17/002G06T2207/10016G06T2207/20056
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Quick Facts
Patent No.
US 12,425,741
App. No.
17/951,961
Granted
Sep 23, 2025
Kind
B2
Abstract

A system and method for detecting, quantifying, and/or measuring motion of an object and correcting for exposure includes providing a processor and at least one video sensor; determining video recording parameters of the at least one video sensor; recording, by the at least one video sensor, video of the object; extracting a data set from the video wherein the data set describes the motion of the object; calculating a frequency transform of the data set for at least one frequency; and performing exposure correction at the at least one frequency based on the recording parameters.

Claims (43)

1. A method for detecting motion of an object and correcting for exposure, comprising:

providing a processor and at least one video sensor;

determining video recording parameters of the at least one video sensor;

recording, by the at least one video sensor, video of the object;

extracting a data set from the video, wherein the data set describes the motion of the object and includes a measurement error;

calculating a frequency transform of the data set for at least one frequency;

performing exposure correction at the at least one frequency based on the recording parameters; and

determining a corrected motion of the object based on the exposure correction;

wherein the motion of the object has a periodic part and the at least one frequency corresponds to a frequency of the periodic part of the motion of the object.

2. The method of claim 1 , further comprising: calculating a frequency spectrum of the data set.

3. The method of claim 2 , further comprising: selecting a frequency of interest and/or receiving a selection of the frequency of interest from the frequency spectrum as the at least one frequency.

4. The method of claim 1 , wherein the data set is a time series digital signal.

5. The method of claim 1 , wherein the data set comprises at least one pixel intensity.

6. The method of claim 5 , further comprising averaging the at least one pixel intensity.

7. The method of claim 1 , wherein exposure correction includes applying a compensation function.

8. The method of claim 1 , wherein exposure correction comprises calculation of a sensitivity function and its reciprocal.

9. The method of claim 1 , wherein the frequency transform is a Fourier transform.

10. The method of claim 2 , further comprising calculating a FFT for the frequency spectrum of the data set.

11. The method of claim 1 , further comprising:

calibrating the at least one video sensor; and

determining a calibration function;

wherein performing exposure correction includes applying the calibration function.

12. The method of claim 11 , wherein calibrating the sensor comprises recording a video of a test object with a known motion magnitude and frequency.

13. The method of claim 11 , further comprising calculating a response function of output-divided-by-input for the sensor at the at least one frequency.

14. The method of claim 1 , wherein an event takes place with respect to the object and the at least one frequency corresponds to an event frequency.

15. The method of claim 1 , wherein the data set describes the motion of the object in the plane perpendicular to the line-of-sight of the video sensor.

16. A method of detecting motion, comprising:

providing a video and recording parameters of the video;

extracting a data set from the video, wherein the data set includes a time series signal describing an event, wherein the event is a motion of an object and wherein the data set includes a measurement error with respect to the motion of the object;

calculating a frequency spectrum of the data set;

selecting a frequency of interest and/or receiving a selection of the frequency of interest from the frequency spectrum;

calculating a frequency transform of the time series signal for the frequency of interest;

performing exposure correction at the frequency of interest using the recording parameters; and

determining an actual motion of the object based on the exposure correction.

17. The method of claim 16 , wherein the motion of the object has a periodic part and the frequency of interest corresponds to a frequency of the periodic part of the motion of the object.

18. The method of claim 16 , wherein exposure correction comprises calculation of a sensitivity function and its reciprocal.

19. A computer program product, comprising a computer readable hardware storage device storing a computer readable program code, the computer readable program code comprising an algorithm that when executed by a computer processor of a computing system implements a method for detecting motion with respect to an object and correcting for exposure, the method comprising:

extracting a data set from a video, wherein the data set includes a time series signal describing an event, wherein the event is a motion of an object and wherein the data set includes a measurement error with respect to the motion of the object;

calculating a frequency spectrum of the data set;

selecting a frequency of interest and/or receiving a selection of the frequency of interest from the frequency spectrum;

calculating a frequency transform of the time series signal for the frequency of interest;

performing exposure correction at the frequency of interest using recording parameters of the video; and

determining an actual motion of the object based on the exposure correction.

Assignments (3)
SECURITY INTEREST Recorded Apr 13, 2026
From: MECHANICAL SOLUTIONS LLC
To: ALTER DOMUS (US) LLC
Reel/Frame 074354/0625 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 29, 2022
From: MARSCHER, WILLIAM D.; FROLOV, SERGEY
To: MECHANICAL SOLUTIONS INC.
Reel/Frame 061257/0055 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 23, 2022
From: SHIELDS, TIMOTHY
To: MECHANICAL SOLUTIONS INC.
Reel/Frame 061198/0938 →
Continuity (2)
Provisional Application 63248020 · Sep 24, 2021
Related Publication 20230103947A1 · Apr 6, 2023
References Cited (28)
US 6515734B1 · Yamada · 2003 [cited by examiner]
US 7196644B1 · Anderson et al. · 2007 [cited by applicant]
US 7843510B1 · Ayer et al. · 2010 [cited by applicant]
US 8786680B2 · Shiratori et al. · 2014 [cited by applicant]
US 10593167B2 · Gervais et al. · 2020 [cited by applicant]
US 20080225125A1 · Silverstein · 2008 [cited by examiner]
US 20090139334A1 · Mustonen · 2009 [cited by applicant]
US 20110080211A1 · Yang et al. · 2011 [cited by applicant]
US 20130002968A1 · Bridge et al. · 2013 [cited by applicant]
US 20130122845A1 · Loewenstein · 2013 [cited by applicant]
US 20140375848A1 · Yamamoto · 2014 [cited by examiner]
US 20150324636A1 · Bentley et al. · 2015 [cited by applicant]
US 20160217587A1 · Hay · 2016 [cited by applicant]
US 20160273957A1 · Bendele et al. · 2016 [cited by applicant]
US 20170169575A1 · Perez Acal et al. · 2017 [cited by applicant]
US 20200175660A1 · Iijima · 2020 [cited by examiner]
WO WO2014024121 · 2014 [cited by applicant]
WO WO2019222833 · 2019 [cited by applicant]
Strickling. W.; Shadow bands during a total solar eclipse; last updated Sep. 17, 2017; http://www.strickling.net/shadowbands.htm; 5 Pages. [cited by applicant]
Chen, J. et al.; Video Camera-Based Vibration Measurement for Civil Infrastructure Applications; Journal of Infrastructure Systems; 23(3); Dec. 8, 2016; 11 Pages. [cited by applicant]
Dunton, T.; “An Introduction to Time Waveform Analysis”; https://reliabilityweb.com/articles/entry/an_introduction_to_time_waveform_analysis; 2020. [cited by applicant]
Braun, S.; The Synchronous (time domain) average revisited; Mechanical Systems and Signal Processing; May 2011; pp. 1087-1102; vol. 25. [cited by applicant]
Gao, Z. et al.; Averaging video sequences to improve action recognition; 2016 9th International Congress on Image and Signal Processing; BioMedical Engineering and Informatics (CISP-BMEI); Datong; 2016, pp. 89-93. [cited by applicant]
Mehrubeoglu, M. et al.; Object tracking using multiple camera video streams; SPIE Proceedings; Apr. 2010; 10 pages; vol. 7724; DOI: 10.1117/12.854091. [cited by applicant]
Petitjean, F. et al.; Summarizing a set of time series by averaging: From Steiner sequence to compact multiple alignment; Theoretical Computer Science; 2012; pp. 76-91; vol. 414, Issue 1. [cited by applicant]
Diego, F.; Probabilistic Alignment of Video Sequences recorded by Moving Cameras; Universitat Autonoma de Barcelona; Jul. 22, 2011; 154 Pages. [cited by applicant]
Lu, C. et al.; A Robust Technique for Motion-Based Video Sequences Temporal Alignment; IEEE Transactions on Multimedia; Oct. 16, 2012; pp. 70-82; vol. 15, Issue 1; DOI: 10.1109/TMM.2012.2225036. [cited by applicant]
Caspi, Y. et al.; A step towards sequence-to-sequence alignment; Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662); Hilton Head Island, SC; Jun. 15, 2000; DOI: 10.1109/C… [cited by applicant]