Difference between revisions of "Computer Vision"
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41. Waymo. (2021). [https://waymo.com/ "Home | Waymo"]. ''Waymo''. Retrieved March 19, 2021. | 41. Waymo. (2021). [https://waymo.com/ "Home | Waymo"]. ''Waymo''. Retrieved March 19, 2021. | ||
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| + | 43. Wiggers, K. (2020, February 27). California DMV Releases autonomous vehicle disengagement reports for 2019. Retrieved from https://venturebeat.com/2020/02/26/california-dmv-releases-latest-batch-of-autonomous-vehicle-disengagement-reports/ | ||
Revision as of 18:54, 24 March 2021
Computer vision is an interdisciplinary scientific field that looks to enable computers to gain high-level understanding from digital images and videos. The field seeks to understand and automate tasks like those accomplished everyday by the human eye. [5]
There are several applications of this field, such as in medicine, machine vision, military, autonomous vehicles, tactile feedback, and more. Tasks for computer vision typically include recognition, identification, detection, scene reconstruction, and image restoration.
Definition
Computer vision is the field of programming in such a way that computers can understand the contents of an image like a human brain. [5] It seeks to convert purely numerical data, represented as the pixels on a screen, and analyze patterns in a fashion where higher-level information can be extracted and understood. It can be summarized as the quantification and automation of the human visual system. [1] Computer vision primarily relies on probabilistic and physics-based models to decipher the inputs, and is still very error prone. [2]
History
Computer vision was first theorized by MIT professor Dr. Lawrence Roberts in his Ph.D. dissertation in 1963. [40] It outlined the extraction of information from known three-dimensional objects from a perspective projection. [3] The process was revisited by David Marr in 1978, who developed techniques to segment and detect edges within images. [4] In one of the most influential publications in computer vision, image recognition from then on was primarily calculated on a basis of perspective understanding as opposed to holistic geometric extrapolation. [5]
Applications
Autonomous Vehicles
Autonomous vehicles are vehicles that are self-guided to some degree. They can range from visual stimuli enhancements for the driver to fully planning and executing a navigation path. [6] Autonomous vehicles can take the form of cars, small delivery robots, submersibles, aircraft, rovers, rockets, missiles, among others. [7] [8] [9] [10] Companies like Waymo are on the frontlines in developing this autonomous driving technology. [41]
Autonomous vehicles have many applications as well. Unmanned aerial vehicles have been used for depositing payloads in urban spaces as well as detecting wildfires. [11] [12] Autonomous submarines have been mapping the ocean and collecting valuable scientific data for years. [13]
Levels of Autonomy
With autonomous cars being the most consumer-facing example, there are many levels of autonomy to classify such vehicles. SAE International has defined 6 different levels of autonomy within vehicles. Level 0 is defined as “No Automation,” or a standard vehicle. Level 1 is defined as simple steering and acceleration assistance, while the majority of control is still up to the driver. Level 2 adds assisted deceleration to the list of autonomous features, with the majority of control still up to the driver. Levels 0-2 are classified as primarily human controlled, whereas levels 3-5 are classified as primarily automated driving.
Level 3 is the first level where the vehicle can operate without driver assistance, but its defining characteristic is that it requires the intervention of the driver when requested. Level 4 can perform entirely autonomously, such that the driver is never asked to intervene except in the case of unknown roadway conditions. Level 5 automation builds upon level 4 in that it can operate entirely autonomously in any given conditions [42].
Total Miles Driven by Autonomous Vehicles [43]
| Company | Total Cars | Miles Driven in 2019 | Disengagements |
|---|---|---|---|
| Waymo | 153 | 1.45 Million | 1 per 11,017 miles |
| GM Cruise | 233 | 831,040 | 1 per 7635 miles |
| Apple | 66 | 7544 | 1 per 1.1 miles |
| Lyft | 20 | 42,930 | 1 per 26 miles |
| Aurora | No Data | 39,729 | 1 per 94 miles |
| Nuro | 33 | 68,762 | 1 per 2022 miles |
| Pony.ai | 22 | 174,845 | 1 per 6476 miles |
| Baidu | 4 | 108,300 | 1 per 18,182 miles |
| Zoox | 58 | 67,015 | 1 per 1596 miles |
Disengagements are circumstances in which the human driver must take control of the vehicle due to a failure in technology or when necessitated for the safe operation of the vehicle.
Surveillance and Facial Recognition
Computer vision has seen a marked rise in applications involving surveillance systems. In the United States, the Federal Bureau of Investigation (FBI) and police department databases have facial recognition information of over 117 million citizens. [15] The majority of these data points are retrieved from driver’s licenses and mugshot records. The FBI’s Next Generation Identification facial recognition program is an extension of the Integrated Automated Fingerprint Identification System, and has an accuracy of about 86%. [16]
Facial recognition and surveillance is prevalent throughout China as well. First enacted in 2005, China’s Skynet system is the largest surveillance system in the world with more than 400 million CCTV cameras installed across the country that keep tabs on nearly every citizen. [17] [18] The system is used for autonomously identifying criminals, tracking citizens’ locations, and controlling civil unrest through apprehension of dissidents. [19] Surveillance techniques have expanded since then to utilize drones which double as law enforcement aid. [20]
Consumer Applications
Facial recognition has seen widespread implementation into consumer products in recent history, with notable examples being Apple’s Face ID and Snapchat’s lenses feature. These features rely on a three-dimensional mask overlaid on a subject's face, constructed using facial features to align the mask to key areas, with Apple’s technology utilizing infrared projection to enable use in the dark. [14]
Computer vision has also seen widespread adoption in the organizing and classification of photos and videos. One such product that has adopted computer vision algorithms for consumer use is Google Photos. [31] Google Photos allows users to view their photos by any number of categorizations, whether it be by objects in the photo, events, pets, and even specific peoples' faces. [31] [32]
Yet another consumer application of computer vision is image restoration. Image restoration is the act of taking a corrupt/noisy image and estimating the clean, originally-intended image. Corruption may come in many forms including noise, camera mis-focus, and motion blur. [37] [38] Restoration is done in an attempt to remove these defects. While this process can be done manually with products like Adobe Photoshop, other products (such as Movavi) are offered that offer a degree of automation (the automation is where computer vision comes in). [39]
Ethics
Autonomous Vehicles
As autonomous navigation systems approach the consumer market, a number of ethical dilemmas are introduced. Like human drivers, autonomous vehicles have to be prepared for emergency situations and act accordingly. One such dilemma, known as the Trolley Problem, is a situation where the vehicle has no other option than to cause an accident. [21] The dilemma occurs when deciding whether to kill a bystander or swerve into a wall and kill the passenger. While contrived and unlikely to occur by conventional means, this dilemma demonstrates the difficulty of translating human decision making skills into code.
Variations of the trolley problem add further complexity to the computation behind autonomous navigation. In another example, in an unavoidable accident situation, the vehicle has to make the decision between hitting a child and an elderly person. The Massachusetts Institute of Technology’s Moral Machine experiment explores this situation using data collected from around the world using societal preferences in who to spare. This study concludes that society would prefer to spare the young when faced with the previous situation. Other results include sparing pedestrians over passengers, sparing more characters over fewer, sparing humans over pets, and sparing higher status individuals over lower class. [22]
Surveillance and Facial Recognition
Facial recognition in surveillance has been widespread for many years now, but has attracted much debate. On one hand, facial recognition technology has led to the successful arrest of numerous criminals. [23] However, it is an imperfect technology and has caused false imprisonment over inaccurate identifications. [24] Additionally, facial recognition algorithms do not operate with the same efficacy for all races and genders. In the case of white males, facial recognition algorithms can have accuracies up to 90%, while it can drop by as much as 34% for Black females. [25]
Facial recognition has also been used in China to track Uighurs, where there is currently a tension between them and the Chinese government. [26] The technology has been used to determine the locations of Uighur citizens so that the Chinese government can relocate them to political re-education camps. [27] These political re-education camps have been labelled internment camps and practice forced sterilization. [28] The United States has denounced the practice, calling it ‘genocide’ and imposing sanctions for their actions. [29] This has sparked further debate about the use of facial recognition technology for extracting ethnic features. [30]
Consumer Applications
There are concerns of privacy regarding the information that can be collected from products like Apple's Face ID and Google Photos. [33] [34] To alleviate these concerns, companies refer users to their privacy statements and give their users assurances. For example, Google Photos promises its users that "face groups and labels in your account are only visible to you," [35] and Apple, tells its users that Face ID "carefully safeguards the privacy and security of a user’s biometric data." [36]
References
1. Medioni, G., & Kang, S. B. (2005). Emerging topics in computer vision. In Emerging topics in computer vision. Upper Saddle River: Prentice Hall. doi:https://sites.rutgers.edu/peter-meer/wp-content/uploads/sites/69/2018/12/rotechcv.pdf
2. Szeliski, R. (2010). Computer vision: Algorithms and applications. In Computer Vision: Algorithms and Applications. Springer.
3. Roberts, L. G. (1980). Machine perception of three-dimensional solids. New York: Garland Pub.
4. Marr, D., & Nishihara, H. (1978). Representation and Recognition of the Spatial Organization of Three-Dimensional Shapes (Vol. 200). Proceedings of the Royal Society of London. doi:http://www.cog.brown.edu/courses/cg195/pdf_files/CG195MaNi78.pdf
5. Huang, T. (1996-11-19). Vandoni, Carlo, E (ed.). [http://cds.cern.ch/record/400313/files/p21.pdf Computer Vision : Evolution And Promise[ (PDF). 19th CERN School of Computing. Geneva: CERN. page 1. doi:10.5170/CERN-1996-008.21. ISBN 978-9290830955.
6. Wen, D., Yan, G., Zheng, N., & Shen, L. (2011). Toward cognitive vehicles. Retrieved from https://ieeexplore.ieee.org/abstract/document/5898448?casa_token=9fQnAkrg9dcAAAAA%3AVmBTXXOtHLmy0NHCCbR7zk0nyRqrAe6EFMXvEVUyRYyjU7gNM_BDxzxXxHPQCI50vvxikK2zdw
7. Frew, E., McGee, T., Kim, Z., Xiao, X., & Jackson, S. (2004, March 6). Vision-based road-following using a small autonomous aircraft. Retrieved from https://ieeexplore.ieee.org/abstract/document/1368106?casa_token=ydmCwxajiuQAAAAA%3AgdOfRAaYq95QsY9nPEc2JT8LI4c5QOeHifDLt0ZRy9jMFSZors5Bzb70J8lPVg1e0ql_DrpyDA
8. Frontiers of engineering: Reports on leading-edge engineering from the 2016 symposium. (2017). Washington, DC: The National Academies Press.
9. Singh, L. (2004). Autonomous missile avoidance using nonlinear model predictive control. AIAA Guidance, Navigation, and Control Conference and Exhibit. doi:10.2514/6.2004-4910
10. Jennings, D., & Figliozzi, M. (2019). Study of Sidewalk autonomous delivery robots and their potential impacts on Freight efficiency and travel. Transportation Research Record: Journal of the Transportation Research Board, 2673(6), 317-326. doi:10.1177/0361198119849398
11. Hadi, G. S., Varianto, R., Trilaksono, B. R., & Budiyono, A. (2015). Autonomous UAV system development for Payload DROPPING MISSION. The Journal of Instrumentation, Automation and Systems, 1(2), 72-77. doi:10.21535/jias.v1i2.158
12. Kumar, M., Cohen, K., & HomChaudhuri, B. (2011). Cooperative control of multiple Uninhabited aerial vehicles for monitoring and fighting wildfires. Journal of Aerospace Computing, Information, and Communication, 8(1), 1-16. doi:10.2514/1.48403
13. Blidberg, D. R. (2001). The development of autonomous underwater VEHICLES (auv); A Brief Summary. Retrieved from https://auvac.org/files/publications/icra_01paper.pdf
14. Mandal, J. K., & Bhattacharya, D. (2020). Emerging Technology in Modelling and Graphics Proceedings of IEM Graph 2018. Singapore: Springer Singapore. doi:https://link.springer.com/content/pdf/10.1007/978-981-13-7403-6.pdf
15. Garvie, C., Bedoya, A., & Frankle, J. (2016, October 18). The perpetual Line-Up. Retrieved from https://www.perpetuallineup.org/
16. Goodwin, G. (n.d.). FACE RECOGNITION TECHNOLOGY DOJ and FBI Have Taken Some Actions in Response to GAO Recommendations to Ensure Privacy and Accuracy, But Additional Work Remains (United States, United States Government Accountability Office).
17. Wang, W. (n.d.). Dark data. Retrieved from https://mfadt.parsons.edu/darkdata/surveillance-madness.html
18. Ng, A. (2020, August 11). China tightens control with facial Recognition, public shaming. Retrieved from https://www.cnet.com/news/in-china-facial-recognition-public-shaming-and-control-go-hand-in-hand/#:~:text=In%20China%2C%20no%20one%20is,jaywalking%20on%20a%20digital%20billboard.
19. Walton, G. (2001). China's golden shield: Corporations and the development of surveillance technology in the People's Republic of China. Montreal: Rights & Democracy, International Centre for Human Rights and Democratic Development.
20. Chen, L. (2019, September 06). Traffic police in China are using drones to give orders from above. Retrieved from https://www.scmp.com/news/china/society/article/3026051/traffic-police-china-are-using-drones-give-orders-above
21. Thomasson, A. L. (2008). General topic: Intentionality and phenomenal consciousness. In General topic: Intentionality and phenomenal consciousness. Peru, IL: Hegeler Inst.
22. Awad, E., Dsouza, S., Kim, R., Schulz, J., Henrich, J., Shariff, A., . . . Rahwan, I. (2018, October 24). The moral machine experiment. Retrieved from https://www.nature.com/articles/s41586-018-0637-6.
23. Sebastian Anthony - Jun 6, 2. (2017, June 06). UK police arrest man via Automatic face-recognition tech. Retrieved from https://arstechnica.com/tech-policy/2017/06/police-automatic-face-recognition/
24. Hill, K. (2020, December 29). Another arrest, and jail time, due to a BAD facial recognition match. Retrieved from https://www.nytimes.com/2020/12/29/technology/facial-recognition-misidentify-jail.html#:~:text=In%202019%2C%20a%20national%20study,on%20bad%20facial%20recognition%20matches.
25. Najibi, A. (2020, October 26). Racial discrimination in face recognition technology. Retrieved from https://sitn.hms.harvard.edu/flash/2020/racial-discrimination-in-face-recognition-technology/
26. Fitzgerald, G. (2020). Chinese authoritarianism and the systematic suppression of the Uighur ethnic minority. Retrieved from https://scarab.bates.edu/asian_studies_theses/7/
27. Raza, Z. (2019). CHINA’S ‘POLITICAL RE-EDUCATION’ camps OF XINJIANG’S UYGHUR MUSLIMS. Asian Affairs, 50(4), 488-501. doi:10.1080/03068374.2019.1672433
28. Ramzy, A., & Buckley, C. (2019, November 16). 'Absolutely no mercy': Leaked files expose how china organized mass detentions of muslims. Retrieved from https://www.nytimes.com/interactive/2019/11/16/world/asia/china-xinjiang-documents.html
29. Wong, E., & Buckley, C. (2021, January 19). U.S. says China's repression of Uighurs Is 'Genocide'. Retrieved from https://www.nytimes.com/2021/01/19/us/politics/trump-china-xinjiang.html
30. X. -d. Duan, C. -r. Wang, X. -d. Liu, Z. -j. Li, J. Wu and H. -l. Zhang, "Ethnic Features extraction and recognition of human faces," 2010 2nd International Conference on Advanced Computer Control, Shenyang, China, 2010, pp. 125-130, doi: 10.1109/ICACC.2010.5487194.
31. Google. (2021). "Get started with Google Photos". Google Photos Help. Retrieved March 12, 2021.
32. Google. (2021). "Search by people, things & places in your photos". Google Photos Help. Retrieved March 12, 2021.
33. Luckerson, V. (2017, May 25). "Why Google Is Suddenly Obsessed With Your Photos". The Ringer. Retrieved March 12, 2021.
34. Toussaint, K. (2017, September 19). "Apple’s face recognition could pose privacy concerns, experts say". Metro US. Retrieved March 19, 2021.
35. Google. (2021). "Search by people, things & places in your photos". Google Photos Help. Retrieved March 12, 2021.
36. Apple. (2021, February 18). "Touch ID and Face ID security". Apple Support. Retrieved March 19, 2021.
37. Castleman, K. R. (1996). Digital Image Processing. Prentice Hall.
38. Jain, A. K. (1989). Fundamentals of Digital Image Processing. Prentice Hall.
39. Movavi. (2021). "Movavi Picverse". Movavi. Retrieved March 19, 2021.
40. Roberts, L. G. (1963). "Machine perception of three-dimensional solids" (dissertation). Institute of Technology, Cambridge, MA. Retrieved March 18, 2021.
41. Waymo. (2021). "Home | Waymo". Waymo. Retrieved March 19, 2021.
42. S. (2020, May 15). SAE J3016 Automated-driving graphic. Retrieved from https://www.sae.org/news/2019/01/sae-updates-j3016-automated-driving-graphic
43. Wiggers, K. (2020, February 27). California DMV Releases autonomous vehicle disengagement reports for 2019. Retrieved from https://venturebeat.com/2020/02/26/california-dmv-releases-latest-batch-of-autonomous-vehicle-disengagement-reports/
