The present disclosure relates to facial detection, license plate reading, vehicle overview, and detection of vehicle make, model, and color.
At security check points, border crossings, high occupancy vehicle (HOV) lanes, and the like, it is desirable to detect faces inside a vehicle, detect and read the license plate of a vehicle, and to identify the make, model, and color of a vehicle. At a traditional checkpoint an officer can ascertain this type of information, including counting occupants in a vehicle. In security applications, it can be desirable to know who the occupants of a vehicle are. An officer can verify this by inspection of identification documents such as a photo ID for each occupant of the vehicle. However, these techniques require each vehicle to stop for inspection before passing through.
The conventional techniques have been considered satisfactory for their intended purpose. However, there is an ever present need for improved systems and methods for detecting, counting, and identifying occupants in vehicles. This disclosure provides a solution for this need.
A system includes a camera defining an optical axis and a field of view defined about the optical axis. An illuminator is mounted offset from the optical axis and directed to illuminate at least a portion of the field of view, wherein the illuminator is operatively connected to the camera to provide illumination during an image capturing exposure of the camera. An image processer is operatively connected to the camera and includes machine readable instructions configured to receive image data representative of an image captured with the camera, perform facial detection to detect at least one face in the image, perform license plate detection/decoding for at least one license plate in the image, and to provide a vehicle overview image.
The machine readable instructions can be configured to output facial detection data for use in facial recognition, to perform license plate reading and to output at least one of a license plate number, region, state, country and/or color of license plate, and to identify make, model, color, year and/or type (class) of a vehicle detected in the image. The model name can be localized based on at least one of geography detected in license plate detection and/or geographical location of the camera. A global positioning sensor can be operatively connected to the image processer, wherein the machine readable instructions are configured to output model name based on location data from the global positioning sensor.
The camera can include optics optically coupled to a sensor, wherein the combined optics and sensor are configured to provide at least 5 megapixels of image data at 280 pixels per foot (30.5 cm). The optics and sensor can be configured to provide a field of view angle of 32.5° by 27.5° (width by height) for imaging vehicles at a distance of 15 feet (4.6 meters). The optics and sensor can be configured to provide a field of view angle of 24.5° by 20.5° (width by height) for imaging vehicles at a distance of 20 feet (6.1 meters). The sensor can be configured for sensitivity in at least one of visible and/or near infrared (NIR).
The illuminator can be separated from the optical axis by a standoff distance configured to avoid overexposure of retroreflective license plate paint. The standoff distance can be two feet (61 cm) and the optical axis can be angled to capture license plate images wherein the license plates are between 15 to 20 feet (4.6 meters to 6.1 meters), inclusive, away from the camera. The camera and illuminator can be configured to properly expose faces through vehicle glass, retroreflective license plates, and vehicle body features all in a single exposure regardless of whether the exposure occurs under noon-day sun or dark night conditions. The camera can be programmed to expose for image capture using an f/1.2 aperture setting, and a 200 microsecond shutter speed day or night. The illuminator can be configured to illuminate at 10° full width at half maximum (FWHM) to illuminate faces inside a vehicle with relatively intense illumination and at the same time illuminate retroreflective license plate of the vehicle with relatively less intense illumination.
The camera can be a first camera of a plurality of cameras, wherein the illuminator is a first illuminator of a plurality of illuminators, and wherein the image processor is configured to control timing of the cameras and illuminators so the first camera exposes for image capture only under illumination from the first illuminator. The first camera and the first illuminator can be mounted to a main pole positioned to face oncoming traffic. The plurality of cameras can include three side pole mounted cameras, one on a common side of a lane of the oncoming traffic with the main pole, and two on an opposite side of the lane. The plurality of cameras can include a rear camera, wherein the plurality of illuminators includes a rear illuminator, and wherein the rear camera and rear illuminator are mounted to a rear pole positioned on an opposite side of a ground loop trigger point from the main pole for imaging rear license plates.
The imaging processor can include a connectivity interface configured to allow remote activation and deactivation of facial detection services, license plate reading services, vehicle overview services, and/or vehicle make, model, and color services, e.g., without needing to change any camera hardware.
These and other features of the systems and methods of the subject disclosure will become more readily apparent to those skilled in the art from the following detailed description of the preferred embodiments taken in conjunction with the drawings.
So that those skilled in the art to which the subject disclosure appertains will readily understand how to make and use the devices and methods of the subject disclosure without undue experimentation, preferred embodiments thereof will be described in detail herein below with reference to certain figures, wherein:
Reference will now be made to the drawings wherein like reference numerals identify similar structural features or aspects of the subject disclosure. For purposes of explanation and illustration, and not limitation, a partial view of an embodiment of a system in accordance with the disclosure is shown in
The system 100 includes a camera 102 defining an optical axis A and a field of view 104 defined about the optical axis A. An illuminator 106 is mounted offset from the optical axis A and directed to illuminate at least a portion of the field of view 104. The illuminator is operatively connected to the camera 102, e.g., directly or by way of an image processor 108 as shown in
With reference now to
With continued reference to
With continued reference to
The camera 102 and illuminator 106 can be configured to properly expose faces 124 even through vehicle glass, retroreflective license plates 122, and body features of the vehicle 110 all in a single exposure regardless of whether the exposure occurs under noon-day sun or dark night conditions. The camera 102 can be programmed to expose for image capture using f/1.2 aperture setting, and a nominal 200 microsecond shutter speed day or night. The exposure settings can be varied with the principle that during the day there is a need to provide enough flash illumination to provide facial images through the windshield without other aspects of the image being saturated. It can be advantageous to shorten the exposure time as much as possible to eliminate the impact of the sun while strobing the illuminator 106 enough that it dominates the signal level in the scene for both day and night imagery. It is possible to implement an autogain control to modify the exposure to acquire the best scene info possible based on the environment. The nominal exposure time of 200 microseconds can be varied based on sensor input.
The illuminator 106 can be configured to illuminate at 10° full width at half maximum (FWHM) in a Gaussian distribution (schematically indicated in
With reference now to
With reference now to
Given the number flashes produced by the illuminators 106, 134, and 144 as a vehicle 110 travels along the lane 136, the image processor 108 (labeled in
With continued reference to
The main pole 128 allows for a single exposure of a vehicle 110 to be used to obtain facial detection data, license plate reading data, vehicle overview data, and vehicle make, model, and color data. The additional poles 130, 138 and their respective cameras 132, 142 and illuminators 134, 144 can optionally be included to provide additional facial detection images and license plate reading images for the same vehicle 110. As shown
The methods and systems of the present disclosure, as described above and shown in the drawings, provide for facial detection, license plate reading, vehicle overview, and vehicle model, make, and color detection all from a single image or exposure of a given vehicle. While the apparatus and methods of the subject disclosure have been shown and described with reference to preferred embodiments, those skilled in the art will readily appreciate that changes and/or modifications may be made thereto without departing from the scope of the subject disclosure.
This application is a continuation of U.S. patent application Ser. No. 16/507,918 filed Jul. 10, 2019, the contents of which are incorporated herein in their entirety.
Number | Name | Date | Kind |
---|---|---|---|
4288819 | Williams | Sep 1981 | A |
5091924 | Bermbach | Feb 1992 | A |
5119236 | Fong | Jun 1992 | A |
5283643 | Fujimoto | Feb 1994 | A |
5343390 | Doi | Aug 1994 | A |
5361840 | Matthews | Nov 1994 | A |
5449864 | Beatty | Sep 1995 | A |
6313946 | Petitto | Nov 2001 | B1 |
6400835 | Lemelson | Jun 2002 | B1 |
6459764 | Chalmers | Oct 2002 | B1 |
6611200 | Pressnall | Aug 2003 | B2 |
6650765 | Alves | Nov 2003 | B1 |
6718049 | Pavlidis | Apr 2004 | B2 |
6856344 | Frantz | Feb 2005 | B2 |
6958676 | Morgan | Oct 2005 | B1 |
6972693 | Brown | Dec 2005 | B2 |
7076088 | Pavlidis | Jul 2006 | B2 |
7092106 | Cox | Aug 2006 | B2 |
7102665 | Chandler | Sep 2006 | B1 |
7132653 | Faubion | Nov 2006 | B2 |
7305108 | Waehner | Dec 2007 | B2 |
7349007 | Millar | Mar 2008 | B2 |
7439847 | Pederson | Oct 2008 | B2 |
7469060 | Bazakos | Dec 2008 | B2 |
7602942 | Bazakos | Oct 2009 | B2 |
7602947 | Lemelson | Oct 2009 | B1 |
7642899 | Alvarado | Jan 2010 | B2 |
7689033 | Xiao | Mar 2010 | B2 |
7786897 | Mves | Aug 2010 | B2 |
7792970 | Bigioi | Sep 2010 | B2 |
8005267 | Chew | Aug 2011 | B2 |
8028903 | Daniel | Oct 2011 | B1 |
8054182 | Cutchis | Nov 2011 | B2 |
8067719 | Herrera | Nov 2011 | B2 |
8155384 | Chew | Apr 2012 | B2 |
8254647 | Nechyba | Aug 2012 | B1 |
8305442 | Millar | Nov 2012 | B2 |
8358343 | Millar | Jan 2013 | B2 |
8509486 | Hsieh | Aug 2013 | B2 |
8604901 | Hoyos | Dec 2013 | B2 |
8830322 | Nerayoff | Sep 2014 | B2 |
8861802 | Bedros | Oct 2014 | B2 |
9087204 | Gormley | Jul 2015 | B2 |
9105128 | Robinson | Aug 2015 | B2 |
9189680 | Komatsu | Nov 2015 | B2 |
9230183 | Bechtel | Jan 2016 | B2 |
9256794 | Braithwaite | Feb 2016 | B2 |
9292754 | Shin | Mar 2016 | B2 |
9396595 | Daniel | Jul 2016 | B1 |
9460598 | Noone | Oct 2016 | B2 |
9471838 | Miller | Oct 2016 | B2 |
9533687 | Lisseman | Jan 2017 | B2 |
9552524 | Artan | Jan 2017 | B2 |
9600712 | Jin | Mar 2017 | B2 |
9613258 | Chen | Apr 2017 | B2 |
9623878 | Tan | Apr 2017 | B2 |
9667627 | Gormley | May 2017 | B2 |
9791766 | Ekin | Oct 2017 | B2 |
9953149 | Tussy | Apr 2018 | B2 |
9953210 | Rozploch | Apr 2018 | B1 |
10146797 | Bataller | Dec 2018 | B2 |
10262126 | Tussy | Apr 2019 | B2 |
10657360 | Rozploch | May 2020 | B2 |
10674587 | Sinitsyn | Jun 2020 | B2 |
10839200 | Nazemi | Nov 2020 | B2 |
10867193 | Hansen | Dec 2020 | B1 |
11087119 | Nazemi | Aug 2021 | B2 |
11196965 | Hansen | Dec 2021 | B2 |
20020092988 | Didomenico | Jul 2002 | A1 |
20030174865 | Vernon | Sep 2003 | A1 |
20030185340 | Frantz | Oct 2003 | A1 |
20030209893 | Breed | Nov 2003 | A1 |
20040070679 | Pope | Apr 2004 | A1 |
20040165750 | Chew | Aug 2004 | A1 |
20040199785 | Pederson | Oct 2004 | A1 |
20040225651 | Musgrove | Nov 2004 | A1 |
20050063566 | Beek | Mar 2005 | A1 |
20050105806 | Nagaoka | May 2005 | A1 |
20050110610 | Bazakos | May 2005 | A1 |
20050271184 | Ovadia | Dec 2005 | A1 |
20060018522 | Sunzeri | Jan 2006 | A1 |
20060028556 | Bunn | Feb 2006 | A1 |
20060055512 | Chew | Mar 2006 | A1 |
20060102843 | Bazakos | May 2006 | A1 |
20060117186 | Yeo | Jun 2006 | A1 |
20060146062 | Kee | Jul 2006 | A1 |
20060284982 | Bigioi | Dec 2006 | A1 |
20070030350 | Wagner | Feb 2007 | A1 |
20070087756 | Hoffberg | Apr 2007 | A1 |
20070112699 | Zhao | May 2007 | A1 |
20070122007 | Austin | May 2007 | A1 |
20070133844 | Waehner | Jun 2007 | A1 |
20080175438 | Alves | Jul 2008 | A1 |
20080211914 | Herrera | Sep 2008 | A1 |
20080285803 | Madsen | Nov 2008 | A1 |
20080297611 | Qiu | Dec 2008 | A1 |
20080298644 | Irmatov | Dec 2008 | A1 |
20090023472 | Yoo | Jan 2009 | A1 |
20090080715 | Van Beek | Mar 2009 | A1 |
20090232365 | Berthilsson | Sep 2009 | A1 |
20090303342 | Corcoran | Dec 2009 | A1 |
20100158380 | Neville | Jun 2010 | A1 |
20110182473 | Wang | Jul 2011 | A1 |
20110242285 | Byren | Oct 2011 | A1 |
20120069183 | Aoki | Mar 2012 | A1 |
20120106806 | Folta | May 2012 | A1 |
20120140079 | Millar | Jun 2012 | A1 |
20120262577 | Wang | Oct 2012 | A1 |
20120328197 | Sanderson | Dec 2012 | A1 |
20130129159 | Huijgens | May 2013 | A1 |
20130147959 | Wang | Jun 2013 | A1 |
20130176285 | Sato | Jul 2013 | A1 |
20130202274 | Chan | Aug 2013 | A1 |
20130236068 | Eshghi | Sep 2013 | A1 |
20130243260 | Burry et al. | Sep 2013 | A1 |
20130251214 | Chung | Sep 2013 | A1 |
20130266193 | Tiwari | Oct 2013 | A1 |
20130266196 | Kono | Oct 2013 | A1 |
20130279757 | Kephart | Oct 2013 | A1 |
20130336538 | Skaff | Dec 2013 | A1 |
20140002617 | Zhang | Jan 2014 | A1 |
20140029005 | Fiess | Jan 2014 | A1 |
20140044348 | Chen | Feb 2014 | A1 |
20140063177 | Tian | Mar 2014 | A1 |
20140132501 | Choi | May 2014 | A1 |
20140132746 | King | May 2014 | A1 |
20140253701 | Wexler | Sep 2014 | A1 |
20140254890 | Bergman | Sep 2014 | A1 |
20140285315 | Wiewiora | Sep 2014 | A1 |
20140320281 | Sager | Oct 2014 | A1 |
20140334684 | Strimling | Nov 2014 | A1 |
20150131872 | Ganong | May 2015 | A1 |
20150186711 | Baldwin | Jul 2015 | A1 |
20150261994 | Yamaji | Sep 2015 | A1 |
20150262024 | Braithwaite | Sep 2015 | A1 |
20150278617 | Oami | Oct 2015 | A1 |
20150286883 | Xu | Oct 2015 | A1 |
20150294144 | Konishi | Oct 2015 | A1 |
20150317535 | Lenor | Nov 2015 | A1 |
20150331105 | Bell | Nov 2015 | A1 |
20150347860 | Meier | Dec 2015 | A1 |
20150357000 | Howell | Dec 2015 | A1 |
20150363655 | Artan | Dec 2015 | A1 |
20160026855 | Mazumdar | Jan 2016 | A1 |
20160063235 | Tussy | Mar 2016 | A1 |
20160171312 | Aoki | Jun 2016 | A1 |
20160171808 | Caterino | Jun 2016 | A1 |
20160178936 | Yang | Jun 2016 | A1 |
20160217319 | Bhanu | Jul 2016 | A1 |
20160239714 | Oami | Aug 2016 | A1 |
20160253331 | Roshen | Sep 2016 | A1 |
20160300410 | Jones | Oct 2016 | A1 |
20160343251 | Lee | Nov 2016 | A1 |
20160379043 | Fazl Ersi | Dec 2016 | A1 |
20170046808 | Parrish | Feb 2017 | A1 |
20170068863 | Rattner | Mar 2017 | A1 |
20170076140 | Waniguchi | Mar 2017 | A1 |
20170106892 | Lisseman | Apr 2017 | A1 |
20180018351 | Fagans | Jan 2018 | A1 |
20180082131 | Li | Mar 2018 | A1 |
20180089528 | Chan | Mar 2018 | A1 |
20180157922 | Miyamoto | Jun 2018 | A1 |
20180181737 | Tussy | Jun 2018 | A1 |
20180189551 | Ranganath | Jul 2018 | A1 |
20180196587 | Bialynicka-Birula | Jul 2018 | A1 |
20180225307 | Kocher | Aug 2018 | A1 |
20180306598 | DeCia | Oct 2018 | A1 |
20180307915 | Olson | Oct 2018 | A1 |
20190089934 | Goulden | Mar 2019 | A1 |
20190180125 | Rozploch | Jun 2019 | A1 |
20190354750 | Nazemi | Nov 2019 | A1 |
20190373157 | Kunihiro | Dec 2019 | A1 |
20220094880 | Hansen | Mar 2022 | A1 |
Number | Date | Country |
---|---|---|
3010922 | Sep 2017 | CA |
102682295 | Sep 2012 | CN |
104024827 | Sep 2014 | CN |
105785472 | Jul 2016 | CN |
10101341 | Jul 2002 | DE |
102015002802 | Aug 2015 | DE |
102014214352 | Jan 2016 | DE |
1482329 | Dec 2004 | EP |
2620896 | Jul 2013 | EP |
2993619 | Mar 2016 | EP |
2395105 | Feb 2013 | ES |
2258321 | Feb 1993 | GB |
2003348573 | Dec 2003 | JP |
4366008 | Nov 2009 | JP |
05997871 | Sep 2016 | JP |
1020050003664 | Jan 2005 | KR |
20090031136 | Mar 2009 | KR |
100964025 | Jun 2010 | KR |
100964886 | Jun 2010 | KR |
101252671 | Apr 2013 | KR |
101514444 | Apr 2015 | KR |
20150137666 | Dec 2015 | KR |
101628390 | Jun 2016 | KR |
20190030960 | Mar 2019 | KR |
200146668 | Jun 2001 | WO |
2004110054 | Dec 2004 | WO |
2012160251 | Nov 2012 | WO |
2013004864 | Jan 2013 | WO |
2014054328 | Apr 2014 | WO |
2014110629 | Jul 2014 | WO |
2015120413 | Aug 2015 | WO |
2016183408 | Nov 2016 | WO |
2017151859 | Sep 2017 | WO |
Entry |
---|
Viisage Technology, Inc. “FaceFINDER 2.5”, Data Sheet, pp. 2 page; https://www.epic.org/privacy/surveillance/cptolight/1105/facefinder.pdf, 2004. |
P. Jonathon Phillips, “Support Vector Machines Applied to Face Reconition”, this is technical report NISTIR 6241, to appear in Advances in Neural Information, Processing Systems 11, eds. M. J. Keams, S. A. Solla, and D. A. Cohn, MIT Press, 1999. |
Huaqing Li, Shaoyu Wang, and Feihu Qi, R. Kiette and J. Zuni'c (Eds.), “Automatic Face Recognition by Support Vector Machines”: IWCIA 2004, LNCS 3322, pp. 716-725, 2004. copyright Springer-Verlag Berlin Heidelberg 2004. |
Jia Hao, Yusuke Morishita, Toshinori Hosoi, Kazuyuki Sakurai, Hitsohi Imaoka, Takao Imaizumi, and Hideki Irisawa, “Large-scale Face Recognition on Smart Devices”, 2013 Second IAPR Asian Conference on Pattern Recognition, 978-1-4799-2190-4/13, copyright 2013 IEEE, DOI 10.1109/ACPR.2013.189. |
F. Z. Chelali, A. Djeradi and R. Djeradi, “Linear discriminant analysis for face recognition,” 2009 International Conference on Multimedia Computing and Systems, Ouarzazate, 2009, pp. 1-10, doi: 10.1109/MMCS.2009.5256630. |
Shishir Bahyal and Ganesh K. Venayagamoorthy, “Recognition of facial expressions using Gabor wavelets and learning vector quantization”, Missouri University of Science and Technology, MO 65409, USA, received in revised form Apr. 26, 2007; accepted Nov. 12, 2007. |
Jin Wei, Zhang Jian-qi, Zhang Xiang, “Face recognition method based on support vector machine and particle swarm optimizatin”, copyright 2010 Elsevier Ltd. All rights reserved, doi: 10.1016/j.eswa.2010.09.108. |
Pavlidis et al., “Automatic Passenger Counting in the High Occupancy Vehicle (HOV) Lanes”, 19 pages, prior to Oct. 20, 2005. |
Dickson, Peter et al. “Mosaic Generation for Under Vehicle Inspection”, Applications of Computer Vision, 2002. (WACV 2002), Pascataway, NJ, Dec. 3, 2022, pp. 251-256. |
International Search Report and Written Opinion for PCT/US06/06708, dated Aug. 29, 2006. |
International Search Report and Written Opinion for PCT/US2019/031755, dated Sep. 5, 2019. |
International Search Report and Written Opinion for PCT/US2018/064444, dated Feb. 21, 2019. |
International Search Report and Written Opinion for PCT/US2020/056429, dated Feb. 9, 2021. |
International Search Report and Written Opinion for PCT/US2020/041195, dated Oct. 21, 2020. |
International Search Report and Written Opinion for PCT/US2022/013783, dated May 16, 2022. |
Number | Date | Country | |
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20210097317 A1 | Apr 2021 | US |
Number | Date | Country | |
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Parent | 16507918 | Jul 2019 | US |
Child | 17119777 | US |