DRAFT 1
DRAFT 1
Introduction
Hook:
Since many decades ago, the smart city life has always been a dream for majority of the urbanites as it will be a giant leap for human beings if everything in the city could be automatically linked together. With the advancement of computer vision technology, this goal becomes achievable because the computer vision provides accurate analysis and intensive connectivity in many sectors such as transportation, security, medical services and so forth.
General information:
Computer vision is a study of artificial intelligence that focuses on how to enable the machine to analyse and identify object as the same way as human does via digital input such as images and videos. On the other hand, a smart city life is the idea of building an urban area with high operation efficiency by implementing the information and communication technology (ICT) into various aspects of life.
Specific information:
In a smart city, it is essential to utilized Computer vision technology as it acts as the “eyes” of the smart city. With this system adapted inside a smart city, it will help in gathering real time data and to promote public welfare by making the smart city more efficient and a safe place to live.
Thesis statement:
In short, computer vision aid in creating smooth traffic, enhance the security system and help in the medical field in a smart city.
Main Point 1：Overcome Traffic Congestion
First of all, a smart and effective traffic management system can be implemented in a smart city with the help computer vision technology.
S1: Autonomous Vehicles
In fact, an autonomous vehicle is inseparable from the computer vision technology. The computer vision technology gives cars the ability to “see” and allow vehicles to detect the obstacles of surrounding and changings of the environment. Through the deep neural network, the computer vision system will analyze the roads, signs, cars, obstacles, and pedestrians in the on-board camera. For example, the self-driving cars can detect the traffic signs correctly to allow the processor of the cars to automatically run the next instructions. It will definitely improve the ability of autonomous cars and thus reduce the number of accidents. The application of this technology will bring unprecedented travel experience to human beings, reshape the transportation system, and build a true era of intelligent transportation.
S2: Vehicle Identification and Analyze Traffic Condition
Through deep learning, the computer vision can also achieve more dimensional recognition. The current "car face recognition" technology nowadays can not only accurately identify the license plate, but also the color, type, heigh, brand year of the vehicle, the person in the vehicle, vehicle windshield and even special signs or characteristic signs at the rear of the vehicle. This function can be applied to some roads with vehicle height restrictions. After the intelligent vehicle acquire the traffic real-time information, it can change a better travel route early to avoid accidents or traffic congestion. In addition, the vehicle identification can also prevent some crimes from happening. When the computer vision system detects the presence of unknown vehicles, it will automatically increase alertness. The usage of real-time traffic monitoring is not only use to traffic statistics but also be used for forecasting accidents and even be used in the inspection of anti-social behavior in public areas (Barthélemy et al., 2019).
S3: Smart Parking System
Through computer vision technology, it is possible to simulate human visual perception where there is a car parked and which place is empty, directly detect and send the data to the platform, and publish it to the parking lot guidance system. Ho et al. (2019) designed a Computer Vision-Based Roadside Occupation Surveillance System (CVROSS) which can improve the transparency of roadside occupancy and vacancy and reduce the probability of double parking through the parking-space calculation and parking space allocation model.
Main Point 2: Enhancing The Security
Furthermore, computer vision technology is capable in ensuring the efficiency of a smart city security system.
S1: Smart surveillance system
Smart surveillance system has utilized the computer vision technology in automizing the process of crime monitoring. For example, in China, Shen Zhen city have installed more than 200-millions of cameras in detecting the whole city, criminals were detected using computer vision technology. By using this technology jaywalker will immediately decrease their social credit once it is spotted. AI detection is also used to detect the gender, ethnicity, age as well as style of walking of the people. AI detection also used in determine the social credit of a person, which allow the government to punish criminals as well as giving tax break for the person who obey the laws. On the other hand, in US, New Orleans have adopted the idea of using Artificial Intelligence in combating crime, which utilize a real time crime center in monitoring the whole city using cameras. The camera will focus on a certain location when there is some incident going on (By detecting the people that call 911). BriefCam, which able to detect and identify the people that pass through as well as the vehicles. Used computer vision/analytics function in determine unusual behavior of people and vehicles as well as tracking people from other part of the camera by detecting the gentle/clothes of the person. Able to search the people/ object with certain by inputting the characteristic of the object.
S2: Airport security
Not only computer vision technology has utilized in smart surveillance system, it also utilizes in other security system such as in airport security system. In this system, it is capable in detecting the behavior of large group of people such as in detecting the suspect's movement, facial expression and facial recognition. It also enhances the check-point security by auto sensing explosive materials, dangerous weapons and firearms, while ignoring others object that is safe. With this the airport security system that utilized the computer vision technology able to track dangerous weapons and counter terrorism.
S3: Security in business
Lastly, computer vision technology also enhances the security in businesses. For example, in Amazon Go, which is a convenient store that used computer vision technology in detecting the customers, as well as automatically deduct the cost of the products that is take off the shelves by the customers and walk out the store. With this system, it is able to prevent shoplifting because it requires the log in of the users using Amazon Prime in order to enter the store and check out the products. Theft detection system also used by various convenient store by applying advance computer algorithms and technology to help business operator to identify threat and theft based on their unusual behavior during check out period or shopping period.
Main Point 3: Increased Healthcare Quality
It has come to the acknowledgement that computer vision is widely beneficial to the healthcare sector which can contribute to better living standards of smart city li
S1: Aiding doctors in diagnoses
Research done has shown improved accuracy of detection by deep learning algorithms. A test was carried out using a cancer detection model which achieved the area of 94.4% under the Receiver Operating Character (ROC) curve, in contrast to experts which had equivalent or lower performance rate. Increased diagnosis accuracy would prevent unnecessary surgery improving the allocation of hospital resources for more optimal uses.
S2: Application in surgical settings
Computer vision has proved to be a helping hand to doctors in surgical scenes. Computer-vision based algorithms are trained to detect surgeon’s hand movement during operating processes to evaluate surgeons’ skills. Such as in laparoscopic surgery, algorithms are trained to assess surgeon's hands and tool movement according to Global Operative Assessment of Laparoscopic Skills (GOALS) criteria. These algorithms can enhance the surgeon's performance through training and skill assessments, so that surgeons can achieve more precision through computer-aided surgeries, leading to better post-operation results.
S3: Help monitors hospital setting and elderly’s living settings
Research found ways to sustain access to effective elderly healthcare and provide more personal services to the growing demand of elderly healthcare through computer vision-based ambience intelligence. Computer vision monitoring provides non-invasive monitoring of elderly in house settings so that help is never out of reach and prevents any acute health problems, such as a model created to detect falls. Long-term monitoring can provide long-term activity analytics of elderly, such as sleeping, walking and sitting, so that healthcare providers have clearer view of elders’ health in order to formulate better assistance. Monitoring can be done through video recording and thermal video data.
With the growing demands of healthcare services, computer vision helps relieve pressure over healthcare providers so that attention is put where it is most needed. Computer vision can be used for administration tasks such as monitoring protocol or paperwork and release resources for more demanding needs.
DRAFT 1
Introduction
Hook:
Since many decades ago, the smart city life has always been a dream for majority of the urbanites as it will be a giant leap for human beings if everything in the city could be automatically linked together. With the advancement of computer vision technology, this goal becomes achievable because the computer vision provides accurate analysis and intensive connectivity in many sectors such as transportation, security, medical services and so forth.
General information:
Computer vision is a study of artificial intelligence that focuses on how to enable the machine to analyse and identify object as the same way as human does via digital input such as images and videos. On the other hand, a smart city life is the idea of building an urban area with high operation efficiency by implementing the information and communication technology (ICT) into various aspects of life.
Specific information:
In a smart city, it is essential to utilized Computer vision technology as it acts as the “eyes” of the smart city. With this system adapted inside a smart city, it will help in gathering real time data and to promote public welfare by making the smart city more efficient and a safe place to live.
Thesis statement:
In short, computer vision aid in creating smooth traffic, enhance the security system and help in the medical field in a smart city.
Main Point 1：Overcome Traffic Congestion
First of all, a smart and effective traffic management system can be implemented in a smart city with the help computer vision technology.
S1: Autonomous Vehicles
In fact, an autonomous vehicle is inseparable from the computer vision technology. The computer vision technology gives cars the ability to “see” and allow vehicles to detect the obstacles of surrounding and changings of the environment. Through the deep neural network, the computer vision system will analyze the roads, signs, cars, obstacles, and pedestrians in the on-board camera. For example, the self-driving cars can detect the traffic signs correctly to allow the processor of the cars to automatically run the next instructions. It will definitely improve the ability of autonomous cars and thus reduce the number of accidents. The application of this technology will bring unprecedented travel experience to human beings, reshape the transportation system, and build a true era of intelligent transportation.
S2: Vehicle Identification and Analyze Traffic Condition
Through deep learning, the computer vision can also achieve more dimensional recognition. The current "car face recognition" technology nowadays can not only accurately identify the license plate, but also the color, type, heigh, brand year of the vehicle, the person in the vehicle, vehicle windshield and even special signs or characteristic signs at the rear of the vehicle. This function can be applied to some roads with vehicle height restrictions. After the intelligent vehicle acquire the traffic real-time information, it can change a better travel route early to avoid accidents or traffic congestion. In addition, the vehicle identification can also prevent some crimes from happening. When the computer vision system detects the presence of unknown vehicles, it will automatically increase alertness. The usage of real-time traffic monitoring is not only use to traffic statistics but also be used for forecasting accidents and even be used in the inspection of anti-social behavior in public areas (Barthélemy et al., 2019).
S3: Smart Parking System
Through computer vision technology, it is possible to simulate human visual perception where there is a car parked and which place is empty, directly detect and send the data to the platform, and publish it to the parking lot guidance system. Ho et al. (2019) designed a Computer Vision-Based Roadside Occupation Surveillance System (CVROSS) which can improve the transparency of roadside occupancy and vacancy and reduce the probability of double parking through the parking-space calculation and parking space allocation model.
Main Point 2: Enhancing The Security
Furthermore, computer vision technology is capable in ensuring the efficiency of a smart city security system.
S1: Smart surveillance system
Smart surveillance system has utilized the computer vision technology in automizing the process of crime monitoring. For example, in China, Shen Zhen city have installed more than 200-millions of cameras in detecting the whole city, criminals were detected using computer vision technology. By using this technology jaywalker will immediately decrease their social credit once it is spotted. AI detection is also used to detect the gender, ethnicity, age as well as style of walking of the people. AI detection also used in determine the social credit of a person, which allow the government to punish criminals as well as giving tax break for the person who obey the laws. On the other hand, in US, New Orleans have adopted the idea of using Artificial Intelligence in combating crime, which utilize a real time crime center in monitoring the whole city using cameras. The camera will focus on a certain location when there is some incident going on (By detecting the people that call 911). BriefCam, which able to detect and identify the people that pass through as well as the vehicles. Used computer vision/analytics function in determine unusual behavior of people and vehicles as well as tracking people from other part of the camera by detecting the gentle/clothes of the person. Able to search the people/ object with certain by inputting the characteristic of the object.
S2: Airport security
Not only computer vision technology has utilized in smart surveillance system, it also utilizes in other security system such as in airport security system. In this system, it is capable in detecting the behavior of large group of people such as in detecting the suspect's movement, facial expression and facial recognition. It also enhances the check-point security by auto sensing explosive materials, dangerous weapons and firearms, while ignoring others object that is safe. With this the airport security system that utilized the computer vision technology able to track dangerous weapons and counter terrorism.
S3: Security in business
Lastly, computer vision technology also enhances the security in businesses. For example, in Amazon Go, which is a convenient store that used computer vision technology in detecting the customers, as well as automatically deduct the cost of the products that is take off the shelves by the customers and walk out the store. With this system, it is able to prevent shoplifting because it requires the log in of the users using Amazon Prime in order to enter the store and check out the products. Theft detection system also used by various convenient store by applying advance computer algorithms and technology to help business operator to identify threat and theft based on their unusual behavior during check out period or shopping period.
Main Point 3: Increased Healthcare Quality
It has come to the acknowledgement that computer vision is widely beneficial to the healthcare sector which can contribute to better living standards of smart city li
S1: Aiding doctors in diagnoses
Research done has shown improved accuracy of detection by deep learning algorithms. A test was carried out using a cancer detection model which achieved the area of 94.4% under the Receiver Operating Character (ROC) curve, in contrast to experts which had equivalent or lower performance rate. Increased diagnosis accuracy would prevent unnecessary surgery improving the allocation of hospital resources for more optimal uses.
S2: Application in surgical settings
Computer vision has proved to be a helping hand to doctors in surgical scenes. Computer-vision based algorithms are trained to detect surgeon’s hand movement during operating processes to evaluate surgeons’ skills. Such as in laparoscopic surgery, algorithms are trained to assess surgeon's hands and tool movement according to Global Operative Assessment of Laparoscopic Skills (GOALS) criteria. These algorithms can enhance the surgeon's performance through training and skill assessments, so that surgeons can achieve more precision through computer-aided surgeries, leading to better post-operation results.
S3: Help monitors hospital setting and elderly’s living settings
Research found ways to sustain access to effective elderly healthcare and provide more personal services to the growing demand of elderly healthcare through computer vision-based ambience intelligence. Computer vision monitoring provides non-invasive monitoring of elderly in house settings so that help is never out of reach and prevents any acute health problems, such as a model created to detect falls. Long-term monitoring can provide long-term activity analytics of elderly, such as sleeping, walking and sitting, so that healthcare providers have clearer view of elders’ health in order to formulate better assistance. Monitoring can be done through video recording and thermal video data.
With the growing demands of healthcare services, computer vision helps relieve pressure over healthcare providers so that attention is put where it is most needed. Computer vision can be used for administration tasks such as monitoring protocol or paperwork and release resources for more demanding needs.
Main Point 4: Counterargument & Refutation – Exposed To Higher Hacking Risk
Counterargument:
Despite the fact that computer vision could improve the smart city life in various aspects, some people may argue that computer vision could be exposed to high hacking risks.
S1: Adversarial Attacks
Unlike humans, a computer vision machine differentiates and identifies objects by analysing the pixels of the input images and comparing it with the image samples in the database. Due to this natural characteristics, a computer vision system may misclassify an object if someone poisons the original images by adding subtle pixels on it (Kong et al., 2021). Hence, some hackers may exploit the bug to pretend to be someone else by wearing adversarial eyeglasses to fool the face recognition system (Sharif et al., 2016). According to Xu et al. (2020), adversarial attack also could occur in the physical real-world situation via physical perturbation on the objects. Eykholt et al. (2018) reported that the success rate of an adversarial attack could achieve 80% and above through low-cost technique such as pasting stickers and posters on road sign to mislead the analysing of computer vision system. This statistics worries the computer scientists as it proved that a simple trick on the autonomous vehicles could probably cause the computer to misjudge the road sign and thus produce a wrong prediction of the next instruction which may consequently lead to an accident.
S2: Privacy Leakage
On the other hand, the implementation of computer vision technology may also increase the risk of privacy leakage. Cross et al. (2019) claimed that the data breach cases are climbing due to the increasingly personal data collection. In July 2015, a gang of hackers broke into the “Ashley Madison” website’s database and exposed the sensitive personal details of about 37 million clients to the public (Cross et al., 2019). If this scenario occurs in smart cities, it will become even worse since countless personal information is collected and stored in the computer vision’s database. This is due to the reason that a computer vision system has to continuously collects data to ensure that their database is always synchronous with the changing of the environment. Additionally, computer vision requires massive data to train the machine learning algorithm in performing analysis with high accuracy and efficiency.
Refutation:
However, these potential hacking risks could be efficaciously reduced by employing different types of countermeasures.
S3: Invention of Countermeasure
According to Park and So (2020), adversarial training with multiple types of adversarial examples could drastically boost the accuracy of the computer vision system against unknown attacks. To support this statement, Kwon and Lee (2021) stated that adversarial attack success rate could be reduced by 27.2% and 24.3% as well as maintaining 98.7 and 91.5% accuracies for the original datasets through adversarial training. By generating diverse adversarial examples as training tools, the robustness of the machine could be effectively enhanced to increase the difficulty of hackers in launching targeted attacks so that the probability of misclassification could be minimized to a large extent. Likewise, data encryption is also one of the countermeasure that have to be adopted by the authorities to ensure the security of the data. Hybrid cryptography methods such as using the combination of Blowfish and ElGamal encryption algorithm could significantly reduce the privacy leak issue as the hackers will be unable to decrypt the information and make it useful even though they managed to steal the data from the database (Anjuli et al., 2020). Data encryption is not only could be perform on numeric data and alphabetic data, but it also could be done on digital image data via image encryption method such as Reversible Data Hiding (RDH) algorithm. In short, the public do not need to worry about the hacking risks of the computer vision technology because there are multiple types of defence mechanisms that could be employed to overcome the weaknesses of the computer vision.
6.0 Conclusion
Summary of Main Points：
To reiterate, computer vision has indeed benefitted and contributed to smart city life, by providing us with better traffic, a more guarded city, and very advanced medical facilities. However, we should not dismiss the fact that this beneficial technology comes with its weaknesses. Every technology is prone to attacks and mistakes, and so is the large database supporting computer vision.
Restate stand and Thesis:
Thus, it is important that proper countermeasures are taken to reduce the risks of potential attacks or mistakes. Given that, the benefits reaped from computer vision far outweigh the possibility of any flaw and it can be concluded that computer vision will bring about many advantages to smart city life.
Final Statement:
The emergence of computer vision technology is the key element to building a smart city. By implementing this technology into various aspects, this can certainly help the citizens to boost their productivity as well as increase their standard of living.