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.
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.
Introduction:
Since many decades ago, the smart city life has always been a dream for majority of the urbanites as it will be huge revolution 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. 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. In the smart city, it is essential to implement computer vision technology as it acts as an “eye” for the city. With this implementation, it achieves the smart city goal by gathering real time data and promoting the public welfare by increasing the efficiency of the system by gathering real. In short, computer vision aid in creating smooth traffic system, enhance the efficiency of a smart security system and contribute to the healthcare system in a smart city.
DRAFT 2
Main Point 1:
First of all, a smart and effective traffic management system can be implemented in a smart city with the help of computer vision technology. 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 analyse the road signs, cars, obstacles, and pedestrians in the on-board camera. For example, the self-driving cars will detect the distance between other cars and maintain a safe distance between each other which is not more than 50 meters. This will definitely reduce the probability of the occurrence of accidents and thus prevent traffic congestion in smart city. Secondly, the computer vision can also achieve more dimensional recognition through deep learning. 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 which without make a legal registration in Road Transport Department Malaysia (JPJ), 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). The computer vision technology is also possible now to simulate human visual perception where the system can detect which car parking lots are empty or full, and then directly send the real-time information and publish it to the parking lot guidance system. For instance, 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. This system successfully reduces the time consumed looking for an empty parking space and the transparency problem of roadside occupancy in smart city. In brief, computer vision will bring unprecedented travel experience to the smart city life by reshaping the transportation system to build a true era of intelligent transportation.
Main Point 2:
First of all, a smart and effective traffic management system can be implemented in a smart city with the help of computer vision technology. 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 analyse the road signs, cars, obstacles, and pedestrians in the on-board camera. For example, the self-driving cars will detect the distance between other cars and maintain a safe distance between each other which is not more than 50 meters. This will definitely reduce the probability of the occurrence of accidents and thus prevent traffic congestion in smart city. Secondly, the computer vision can also achieve more dimensional recognition through deep learning. 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 which without make a legal registration in Road Transport Department Malaysia (JPJ), 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). The computer vision technology is also possible now to simulate human visual perception where the system can detect which car parking lots are empty or full, and then directly send the real-time information and publish it to the parking lot guidance system. For instance, 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. This system successfully reduces the time consumed looking for an empty parking space and the transparency problem of roadside occupancy in smart city. In brief, computer vision will bring unprecedented travel experience to the smart city life by reshaping the transportation system to build a true era of intelligent transportation.
Main Point 3:
Moreover, computer vision can bring about meaningful changes to the medical sector, contributing better healthcare quality to smart city life. Research done has shown improved accuracy of detection by deep learning algorithms (Khemasuwan et al., 2020). 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 who had equivalent or lower performance rates (Khemasuwan et al., 2020). This improved accuracy rate is beneficial to patients since it would provide doctors with a correct understanding of patients’ health and allow them to make better clinical decisions to obtain more positive health outcomes. Consequently, accurate diagnosis prevents any unnecessary invasive surgeries so that resources could be freed up for more optimal use. Computer vision also proved its value in surgical scenes as surgeons achieve more accuracy and precision with computer-aided surgeries. Computer vision-based algorithms are trained to detect a surgeon’s hand movements during operating processes to evaluate the surgeon’s skills (Esteva et al., 2021). These evaluations proved to be more objective than individual evaluations by surgical experts and could boost surgeons’ performance through consistent training and skill assessments (Azari et al., 2019; Esteva et al., 2021). Technical skills play a major part in surgery outcomes, thus seeing an increase of skills would largely contribute to better surgery results and allow patients to endure less postoperative pain resulting in increased satisfaction. In addition, a more personalized and effective elderly healthcare could be sustained through computer vision-based ambience intelligence. Ambience intelligence 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 (Núñez-Marcos et al., 2017). Long-term monitoring could provide long-term activity analytics of the elderly, such as sleeping, walking, and sitting so that healthcare providers have a clearer view of elders’ health to formulate better assistance. Monitoring can be done through video recording and thermal video data (Luo et al., 2018). Ambience Intelligence is also used in ICU for safety-critical behaviors and for administrative tasks such as protocol monitoring or paperwork to release resources for more demanding needs (Esteva et al., 2021). 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; hence, provides smart city life with sustainable effective healthcare.
Main Point 4:
Despite the fact that computer vision could improve smart city life in various aspects, some people may argue that computer vision could be exposed to high hacking risks. Unlike humans, a computer vision machine differentiates and identifies objects by analysing the pixels of the input images and comparing them with the image samples in the database. Due to these natural characteristics, a computer vision system may misclassify an object if someone poisons the original images by adding subtle pixels on them (Kong et al., 2021). For instance, a hacker could pretend to be someone else by wearing adversarial eyeglasses which could modify the pixels of images to fool the face recognition system (Sharif et al., 2016). In smart city, this scenario would seriously threaten the criminal tracking network and immigration process at the airport as some criminals may exploit the bug to escape themselves from arresting by smuggling to another country. Adversarial attack not only could be done via digital perturbation but also could be done via physical perturbation on the objects in the physical real-world situation. Eykholt et al. (2018) and Xu et al. (2020) reported that the success rate of an adversarial attack could achieve 80% and above through low-cost techniques such as pasting stickers on a road sign to mislead the computer vision system. This statistic 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 for the next instruction which may consequently lead to accidents. However, these potential hacking risks could be efficaciously reduced by employing different types of countermeasures. According to Park and So (2020), adversarial training with multiple 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 the adversarial attack success rate could be reduced by 27.2% and 24.3% for various adversarial examples while 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. Therefore, the public should not worry about that computer vision would become the security hole of the smart city because there are multiple types of defence mechanisms that could be employed to overcome the weaknesses of computer vision.
Conclusion:
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. 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. 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.