IP Library Granted Patent US 12,450,756
Granted Patent B2
US 12,450,756 · App. 18/428,153 · Granted Oct 21, 2025

Methods and apparatus for detecting unrecognized moving objects

Inventors: Nicholas Setzer (Boston, MA); Shekhar Bangalore Sastry (Arlington, MA)
Assignee: SimpliSafe, Inc.
G06T7/248G01J5/0025G06T7/277H04N7/183H04N7/188G06T2207/10016G06T2207/10048
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Quick Facts
Patent No.
US 12,450,756
App. No.
18/428,153
Granted
Oct 21, 2025
Kind
B2
Abstract

Security methods and apparatus. In one example, a method includes detecting a motion event in a scene using a motion detector, based on detecting the motion event, acquiring a plurality of images of the scene using an image capture device, applying a motion detection process to the plurality of images to detect motion in the scene, applying an object detection process to at least one of the images to detect an object in the scene, pairing the motion with the object to locate a moving object, identifying the moving object as an unrecognized object, and based at least in part on identifying the moving object as an unrecognized object, triggering a video recording of the scene using the image capture device.

Claims (57)

1. A method comprising:

acquiring an image of a scene;

pairing motion with an object by determining that a first bounding box and a second bounding box at least partially overlap within the image, the first bounding box indicative of the motion in the scene, the second bounding box indicative of the object in the image, and the pairing indicating that the object is a moving object; and

initiating, based at least in part on the pairing, recording of a video sequence with an image capture device.

2. The method of claim 1 , further comprising:

applying an object detection process to the image to produce the second bounding box.

3. The method of claim 2 , wherein applying the object detection process includes using a trained artificial neural network to locate the object in the image.

4. The method of claim 1 , wherein acquiring the image includes acquiring a first image and a second image.

5. The method of claim 4 , further comprising:

comparing the first and second images to detect the motion in the scene; and

producing the first bounding box based on the comparing.

6. The method of claim 5 , wherein comparing the first and second images includes:

converting the first and second images from color to greyscale to produce first and second greyscale images;

comparing pixel intensities in the first and second greyscale images; and

detecting the motion based on differences in at least some of the pixel intensities in the first and second greyscale images exceeding a value.

7. The method of claim 6 , further comprising downsizing a frame size of the first and second greyscale images to produce a first reduced greyscale image and second reduced greyscale image;

wherein comparing pixel intensities in the first and second greyscale images includes comparing pixel intensities in the first and second reduced greyscale images.

8. The method of claim 1 , further comprising:

controlling the image capture device to record the video sequence for a period of time; and

after the period has ended, deactivating the image capture device.

9. The method of claim 1 , further comprising:

identifying the moving object as an unrecognized object; and

based at least in part on identifying the moving object as an unrecognized object, initiating recording of the video sequence with the image capture device.

10. The method of claim 9 , further comprising:

based on identifying the moving object as an unrecognized object, acquiring at least one additional image of the scene; and

processing the at least one additional image to confirm identification of the moving object as an unrecognized object.

11. A device comprising:

a camera;

at least one processor; and

a data storage device storing instructions that when executed by the at least one processor cause the device to

acquire a first image of a scene,

pair motion with an object to generate a pairing by determining that a first bounding box and a second bounding box at least partially overlap within the first image, the first bounding box indicative of the motion in the scene, the second bounding box indicative of the object in the first image, and the pairing indicating that the object is a moving object, and

based at least in part on the pairing, cause the camera to record begin recording video.

12. The device of claim 11 , wherein the data storage device further stores instructions that when executed by the at least one processor cause the device to:

apply an object detection process to the first image to produce the second bounding box.

13. The device of claim 12 , wherein to apply the object detection process, the at least one processor is configured to operate an artificial neural network detect the object.

14. The device of claim 11 , wherein the data storage device further stores instructions that when executed by the at least one processor cause the device to acquire a second image.

15. The device of claim 14 , wherein the data storage device further stores instructions that when executed by the at least one processor cause the device to apply a motion detection process to the first and second images to produce the first bounding box.

16. The device of claim 15 , wherein to apply the motion detection process, the data storage device further stores instructions that when executed by the at least one processor cause the device to:

convert the first and second images from color to greyscale to produce first and second greyscale images;

compare pixel intensities in the first and second greyscale images; and

detect the motion based on differences in at least some of the pixel intensities in the first and second greyscale images exceeding a value.

17. The device of claim 16 , wherein to apply the motion detection process, the data storage device further stores instructions that when executed by the at least one processor cause the device to:

downsize a frame size of the first and second greyscale images to produce a first reduced greyscale image and second reduced greyscale image; and

compare pixel intensities in the first and second reduced greyscale images.

18. The device of claim 11 , wherein the camera is configured to record the video for a period of time, and wherein the data storage device further stores instructions that when executed by the at least one processor cause the device to:

after the period has ended, deactivate the camera.

19. The device of claim 11 , wherein the data storage device further stores instructions that when executed by the at least one processor cause the device to:

identify the moving object as an unrecognized object; and

based at least in part on identifying the moving object as an unrecognized object, cause the camera to begin recording the video.

20. The device of claim 19 , wherein the data storage device further stores instructions that when executed by the at least one processor cause the device to:

based on identifying the moving object as an unrecognized object, acquire at least one additional image of the scene; and

process the at least one additional image to confirm identification of the moving object as an unrecognized object.

21. A computer program product comprising one or more non-transitory computer-readable media storing sequences of instructions executable to control a security camera disposed at a location, the sequences of instructions comprising instructions to:

acquire an image of a scene;

pair motion with an object to generate a pairing by determining that a first bounding box and a second bounding box at least partially overlap within the image, the first bounding box indicative of the motion in the scene, the second bounding box indicative of the object in the image, and the pairing indicating that the object is a moving object; and

based at least in part on the pairing, cause an image capture device to begin recording video.

Assignments (2)
SECURITY INTEREST Recorded Nov 12, 2025
From: SIMPLISAFE, INC.
To: CAPITAL ONE, NATIONAL ASSOCIATION, AS COLLATERAL AGENT
Reel/Frame 073579/0854 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 16, 2024
From: SETZER, NICHOLAS; SASTRY, SHEKHAR BANGALORE
To: SIMPLISAFE, INC.
Reel/Frame 067117/0166 →
Continuity (3)
Continuation 18348008 · Jul 6, 2023
Provisional Application 63482218 · Jan 30, 2023
Related Publication 20250014195A1 · Jan 9, 2025
References Cited (72)
US 6580812B1 · Harrington · 2003 [cited by applicant]
US 7474767B2 · Sen et al. · 2009 [cited by applicant]
US 8189049B2 · Lopota et al. · 2012 [cited by applicant]
US 8233094B2 · Subbotin et al. · 2012 [cited by applicant]
US 9521606B1 · Costa et al. · 2016 [cited by applicant]
US 9906722B1 · Gigot · 2018 [cited by applicant]
US 10333923B2 · Johri · 2019 [cited by applicant]
US 10402643B2 · James et al. · 2019 [cited by applicant]
US 10504240B1 · Solh et al. · 2019 [cited by applicant]
US 11004209B2 · Chen et al. · 2021 [cited by applicant]
US 11017240B2 · Shekhar · 2021 [cited by examiner]
US 11217076B1 · Siminoff et al. · 2022 [cited by applicant]
US 11257226B1 · Solh et al. · 2022 [cited by applicant]
US 11288551B2 · Desai et al. · 2022 [cited by applicant]
US 11336869B2 · Yao et al. · 2022 [cited by applicant]
US 11776276B1 · Muron et al. · 2023 [cited by applicant]
US 11803973B1 · Xu et al. · 2023 [cited by applicant]
US 11922642B1 · Setzer et al. · 2024 [cited by applicant]
US 11922669B1 · Sastry et al. · 2024 [cited by applicant]
US 20030236622A1 · Schofield · 2003 [cited by examiner]
US 20050198600A1 · Hasegawa · 2005 [cited by applicant]
US 20120314901A1 · Hanson et al. · 2012 [cited by applicant]
US 20130293460A1 · Kaplan et al. · 2013 [cited by applicant]
US 20160026890A1 · Gupta et al. · 2016 [cited by applicant]
US 20160042621A1 · Hogg et al. · 2016 [cited by applicant]
US 20170109613A1 · Kolavennu et al. · 2017 [cited by applicant]
US 20180285650A1 · George et al. · 2018 [cited by applicant]
US 20180288397A1 · Lee · 2018 [cited by applicant]
US 20180349708A1 · Van Hoof et al. · 2018 [cited by applicant]
US 20190087644A1 · Shieh et al. · 2019 [cited by applicant]
US 20190087646A1 · Goulden et al. · 2019 [cited by applicant]
US 20190130580A1 · Chen · 2019 [cited by examiner]
US 20190311201A1 · Selinger et al. · 2019 [cited by applicant]
US 20190318171A1 · Wang et al. · 2019 [cited by applicant]
US 20190391254A1 · Asghar · 2019 [cited by examiner]
US 20200005468A1 · Paul · 2020 [cited by examiner]
US 20200082544A1 · Zhu et al. · 2020 [cited by applicant]
US 20200228760A1 · Yen et al. · 2020 [cited by applicant]
US 20200342748A1 · Tournier et al. · 2020 [cited by applicant]
US 20210089841A1 · Mithun et al. · 2021 [cited by applicant]
US 20210090427A1 · Hass · 2021 [cited by examiner]
US 20210158048A1 · Lee et al. · 2021 [cited by applicant]
US 20210329193A1 · Wu et al. · 2021 [cited by applicant]
US 20210365707A1 · Mao et al. · 2021 [cited by applicant]
US 20210390696A1 · Iwase et al. · 2021 [cited by applicant]
US 20220027637A1 · Madden · 2022 [cited by examiner]
US 20220201320A1 · Karunaratne et al. · 2022 [cited by applicant]
US 20220222477A1 · Shen et al. · 2022 [cited by applicant]
US 20220301187A1 · Watanabe · 2022 [cited by applicant]
US 20220335816A1 · Dice et al. · 2022 [cited by applicant]
US 20230014948A1 · Guan · 2023 [cited by applicant]
US 20230156323A1 · Hanzawa · 2023 [cited by applicant]
US 20230306712A1 · Lee et al. · 2023 [cited by applicant]
US 20240031663A1 · Morgan · 2024 [cited by applicant]
US 20240046696A1 · Zhang · 2024 [cited by examiner]
US 20240249420A1 · Xu et al. · 2024 [cited by applicant]
US 20240257360A1 · Xu et al. · 2024 [cited by applicant]
US 20240257521A1 · Sastry et al. · 2024 [cited by applicant]
WO 2010084902A1 · 2010 [cited by applicant]
WO 2024155336A1 · 2024 [cited by applicant]
WO 2024163178A1 · 2024 [cited by applicant]
WO 2024163278A1 · 2024 [cited by applicant]
Suman Tewary et al, Hybrid multi-resolution detection of moving targets in infrared imagery, Infrared Physics & Technology, Jul. 22, 2014, 173-183, 67. [cited by applicant]
Gustavo H.F. De Carvalho et al., Anomaly detection with a moving camera using multiscale video analysis, Multidim Syst Sign Process, (2019), 311-342, 30. [cited by applicant]
Nazir Sajid et al., “Person Detection with Deep Learning and loT for Smart Home Security on Amazon Cloud”, 2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), IE… [cited by applicant]
Shahid Aasma et al., “Computer vision based intruder detection framework (CV-IDF)”, 2017 2nd International Conference on Computer and Communication Systems (ICCCS), IEEE, Jul. 11, 2017, pp. 41-45. [cited by applicant]
F. C. Akyon et al. “Slicing Aided Hyper Inference and Fine-Tuning for Small Object Detection,” 2022 IEEE International Conference on Image Processing (ICIP), Bordeaux, France, Oct. 16-19, 2022, pp. 966-970. [cited by applicant]
Ren et al., “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, No. 6, Jun. 6, 2017. [cited by applicant]
Wang et al., “Improved Object Detection Algorithim Based on Faster RCNN”, Journal of Physics: Conference Series, 2022. [cited by applicant]
Zhang et al., “Moving Objective Detection and Its Contours Extraction Using Level Set Method”, IEEE International Conference on Control Engineering and Communication Technology, 2012, pp. 778-781. [cited by applicant]
Agrawal et al., “ An improved Gaussian Mixture Method based Background Subtraction Model for Moving Object Detection in Outdoor Scene”, IEEE Fourth International Conference on Electrical, Computer and Communication Tech… [cited by applicant]
International Search Report and Written Opinion received for PCT Application No. PCT/US2024/011937, dated Apr. 9, 2024. 15 pages. [cited by applicant]