Embedded vision plays a crucial role in shaping smart cities, which are constantly developing and will continue to play an important role in humanity’s present and future. Cities are multi-hubs of business, technology, and culture, but they also drive pollution and inequality, generating 80 percent of global GDP and creating more than 70 percent of yearly global carbon emissions. Promoting and sustaining economic development, prosperity, and equity is a critical goal and a serious task for smart cities.
The future of embedded vision in smart cities is promising, as cities continue to grow and face challenges in managing resources like the environment, traffic, security, and administration. AI technologies with embedded cameras enable the creation of sustainable smart cities by managing these resources effectively.
Benefits of Embedded Vision in Smart Cities
To attract and expand investments in urban infrastructure, respective authorities need to highlight smart city digitization. Embedded vision is a key component of smart city solutions, utilizing low-power sensors, cameras, and AI algorithms to monitor the efficiency of the city. These technologies simplify the integration and management of assets for city managers.
Applications of Embedded Vision in Smart Cities
Computer vision is vital in smart city management because it serves as the city’s “eyes.” Some of the most important computer vision applications for smart cities are as follows:
Smart Traffic and Bicycle Monitoring
Increased vehicles typically equal more traffic congestion, longer travel times, accidents, local air pollution, and carbon emissions, not to mention a general sense of tiredness, tension, and worry. An edge-enabled computer vision system may use new or existing street cameras to capture a real-time image of traffic conditions, which is then correlated with particularly trained machine learning algorithms.
Intelligent Parking Monitoring
The use of computer vision technology can assist authorities in providing improved services, such as car park administration. Drivers hunting for available parking spaces account for a significant share of daily traffic delays and emissions in a crowded metropolis. Using parking lot AI cameras, it is possible to identify vehicles entering or exiting a parking place, recognize plate numbers automatically, and record the parking duration. All the information presented above may be transferred into a single, cloud-based database, allowing local officials to save money on parking enforcement.
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Monitoring of Public Space
Cities are responsible for public infrastructure such as water treatment and distribution systems, electrical networks, telephone equipment, street lighting, roads, tunnels, and bridges. The certainty of a significant number of infrastructure-related incidents, on the other hand, can put even the best-resourced teams under strain, resulting in a response that is neither swift nor optimum. City officials, on the other hand, will be able to make effective and efficient decisions more quickly if a cloud-based system has access to footage from all local CCTV networks, analyses it, and automatically advises on appropriate measures.
Quality Control
Smart camera applications in smart factories provide a scalable solution for automated visual inspection and quality control of production processes and assembly lines. Deep learning uses real-time item detection in this scenario to outperform time-consuming manual inspection. Machine learning technologies are more robust than traditional machine vision systems, and they do not need expensive specialized cameras or controlled settings. As a consequence, AI vision technologies might be applied in a wide range of settings and factories.
Public Safety and Health
As the COVID outbreak illustrated, there are times when municipal officials must respond to completely unexpected and novel circumstances. Computer vision systems can assist public services (such as police stations, hospitals, water treatment facilities, and traffic management control rooms) adapt to changing regulations, correctly notify citizens, identify noncompliance clusters, and take appropriate corrective action. A violation of a health protocol, for example, in a public place, may be detected, analyzed, and managed more promptly and precisely, minimizing the risk of runaway dangers to the local population.
Governance and safety
Governments are investing heavily in smart cities for a variety of reasons. A primary motivator of smart city development is the opportunity to improve law enforcement and civilian safety. Local or federal governments can employ computer vision for smart city efforts to achieve this goal. Image sensors and facial recognition software help develop a citizen database. It makes identifying and apprehending an illegal citizen easier, as well as knowing the identity of injured persons in the case of an accident. The use of computer vision in smart cities enables residents to live in a safe and secure environment.
Medical Skill Development
Computer vision applications are used in self-learning platforms to assess expert learners’ abilities. The introduction of simulation-based surgical teaching systems, for example, has benefitted surgical education. Furthermore, the notion of action quality evaluation enables the creation of computer systems that automatically evaluate the performance of surgical students. As a consequence, individuals may acquire helpful feedback information that may aid in their skill development.
Future and Possibilities
According to statistics by international organizations, cities currently hold more than half of the world’s population and will hold more than two-thirds by 2050. Different resources, including the environment, traffic, security, and administration, can be managed using smart technologies. In other words, an AI infrastructure enables the creation of a sustainable smart city for its citizens. The environment, energy, transportation, and security are just a few of the industries where AI technologies and resources are used.
Summing Up
The deployment of embedded vision systems can significantly enhance public services and the lives of inhabitants and visitors in smart cities. These technologies with embedded camera reduce monitoring costs, enable reliable predictive analysis, and facilitate faster decision-making. The potential for artificial intelligence to enhance people’s quality of life in smart cities is vast, and it requires a clear political framework to build such infrastructure.
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