Computer Vision Use Cases In Government Sector
Computer Vision, which is the ability of machines to interpret and understand visual information from the world, has been transforming numerous industries for the past few years.
Recently, this disruptive technology has also caught the attention of governments and agencies, showcasing its utility in addressing an array of challenges. It has proven instrumental in enhancing public safety and streamlining operations within the public sector.
This article will delve into some of the most innovative applications of computer vision in the public sphere and examine how this groundbreaking technology is revolutionizing governmental functions.
But before we proceed, let's first shed light on the primary benefits of Computer Vision:
One of the most profound impacts of deep learning within the public sector lies in its capacity to bolster decision-making. Leveraging deep learning algorithms, public sector organizations can process massive volumes of data, yielding valuable insights that inform strategic decisions. For instance, these intricate algorithms can analyze medical imagery, detect patterns, and thereby assist physicians in diagnosing patients' underlying conditions.
Optimized Resource Distribution
Computer vision is a crucial tool for optimizing resource utilization. By analyzing data related to population demographics, crime statistics, and traffic patterns, public sector organizations can tailor their resource allocation more effectively. For instance, a study by the Rand Corporation highlighted the potential of predictive policing algorithms, reducing theft incidents by an impressive 13.6%.
Improved Public Services
Deep learning algorithms can tap into data from diverse sources such as social media to identify emerging trends and assess public sentiment. This valuable information facilitates immediate enhancements to public services. Thus, through the data-driven insights gained, public services can continually improve, rapidly adapting to the shifting needs and sentiments of the populace.
Major Applications of Computer Vision in the Public Sector
Let's look at some use cases of computer vision:
One of the most salient applications of computer vision resides in law enforcement. Worldwide, police departments leverage computer vision technologies for public safety, crime prevention, and rapid resolution of criminal cases.
NYPD's use of ShotSpotter
Since 2011, the New York Police Department (NYPD) has been using ShotSpotter, a computer vision system, to find gunshots in real time.
The system uses a network of sound detectors on roofs and streetlights all over the city to find and pinpoint gunfire. When a gunshot is heard, the system notifies the police dispatch center and tells them where the shot was fired.
This helps officers respond more quickly and correctly. With this technology, the NYPD has been able to cut down on gun violence and make some of the city's most dangerous neighborhoods safer for everyone.
Dubai Police's adoption of facial recognition tech
In the same way, since 2018, the Dubai Police Department has been using facial recognition technology to track down criminals and suspects.
Face-recognition cameras have been put up in public places like shopping malls, airports, and other busy places to take pictures of people's faces. The images are then compared to a database of known criminals and suspects. If there is a match, the police are notified.
The police in Dubai have been able to find and catch hundreds of criminals and fugitives with the help of this technology. This has made the city safer for both residents and visitors.
These instances underscore the potential of computer vision in elevating public safety and security. Nonetheless, law enforcement agencies worldwide continue to explore innovative applications of this technology for community protection.
Tackle Traffic Congestion
Most cities around the world have a lot of trouble with traffic jams. According to the INRIX Global Traffic Scorecard, traffic jams cost the U.S. economy $88 billion yearly, and drivers spend an average of 54 hours each year stuck in traffic.
Computer vision's intervention in traffic management could drastically decongest roadways. It empowers city officials with real-time traffic data, facilitating effective decision-making.
Adaptive Traffic Control System (ATCS)
The Adaptive Traffic Control System (ATCS) in Pittsburgh is a shining example of computer vision's application in traffic management. This system uses real-time traffic data from cameras and sensors to modify traffic signal timings, thereby mitigating traffic congestion and enhancing flow.
The ATCS has been credited with reducing travel time by up to 25% and pollution by up to 20%.
Computer vision also comes into play in accident management. For instance, San Diego has implemented a system that leverages computer vision to automatically trigger emergency response in case of a road accident.
This innovation has streamlined emergency response, fostering safer driving conditions for all.
Optimizing public transportation
Computer vision can also elevate public transportation efficiency and safety while reducing traffic.
By analyzing passenger flow and demand in real time, computer vision can optimize bus and train schedules, minimize wait times, and enhance overall system efficiency.
Computer vision significantly aids the public sector in identifying criminals and their locations. Face recognition technology powered by computer vision algorithms can help law enforcement agencies accurately locate suspects and criminals.
FBI's Next Generation Identification (NGI)
The FBI's Next Generation Identification (NGI) system exemplifies this technology. It uses computer vision algorithms to look at fingerprint, iris, and facial recognition data. Several high-profile crimes have been solved with the help of this system.
Video surveillance can also be used to track down suspects and criminals, in addition to facial recognition. By using machine learning algorithms to look at surveillance footage, police can find patterns of movement, track suspects' movements, and even guess where they might be going.
Computer vision was used to search for the Boston Marathon bombers in 2013. This is an excellent example of how this technology can be used. The FBI used surveillance footage from private businesses and government buildings, among other places, to track the suspects' movements and catch them.
Even though facial recognition technology has raised concerns about privacy and civil liberties, it is still an essential tool for fighting crime. Moreover, as computer vision and machine learning improve, these technologies will likely become more accurate and valuable in the coming years.
Computer vision is also used in the public sector to track how diseases spread, especially during outbreaks. Using computer vision can help find outbreaks and stop them as soon as they start, saving lives in the long run.
Tackling the pandemic
During the COVID-19 pandemic, for example, China used computer vision to find people who had been near confirmed cases. Face recognition technology was used to find people who didn't have masks on or didn't follow the rules for social distancing. The system also used thermal cameras to check if people had fevers, a common sign of Covid-19.
The same kind of technology has also been used in other places. For example, in Singapore, the government used an app called TraceTogether to find people who had close contact with confirmed cases. This app used Bluetooth signals to find people close to confirmed cases and had been in touch with them. This made it easy for health workers to find people who might have been exposed to the virus and put them in a separate area.
Monitor disease outbreaks
Computer vision can also look for signs of disease outbreaks in public places. For instance, researchers at Carnegie Mellon University made a system that can pick up on coughing and sneezing sounds in public places. This can be used to spot possible outbreaks of diseases like the flu or COVID-19 so that public health officials can act quickly to stop the disease from spreading.
Using computer vision to track how diseases spread can help public health workers find and stop outbreaks quickly. This can save lives and cut down on the cost of pandemics.
Border security is vital for the safety and well-being of every country. Unfortunately, traditional border security methods, like walls and security guards, can't keep track of everyone, everything, and everything that goes across the border. Computer vision technology has changed the way border security works in a big way.
Tackle illegal cross-border activities.
With the help of computer vision technology, border security agencies can find and keep track of illegal crossings, smuggling, and other illegal activities. Cameras with computer vision algorithms can track people, cars, and goods crossing the border. In addition, they can remember people's faces, license plates, and other important details about the vehicles, which can help them spot possible threats.
Computer vision technology can also help find fake documents and goods. It can look over the documents and tell if they are real or not. Scanning vehicles and goods at border checkpoints can also find illegal items like drugs, weapons, and other things that shouldn't be there.
Many countries, like the U.S., Mexico, and Israel, have already used computer vision technology at their borders to keep people safe. For example, the U.S. has put up self-driving surveillance towers with computer vision technology to keep an eye on the border. Radar, cameras, and other sensors are used in these towers to find any possible threat.
Also, people are considering using drones with computer vision algorithms to monitor the borders. These drones can fly over the borders and collect data in real-time, which can be used to find potential threats.
As the repercussions of climate change, pollution, and additional environmental issues exacerbate, it is vital to continuously monitor these transformations, strategizing proactive measures to mitigate further damage. Unfortunately, keeping an eye on the environment the traditional way is complex, expensive, and takes a lot of time.
Integrating computer vision in wildlife monitoring enables real-time insights into environmental changes. It automates tasks such as species identification, behavior analysis, and habitat assessment. By tracking populations and detecting threats, it enhances conservation efforts for more proactive decision-making.
For instance, drones equipped with computer vision capabilities can monitor wildlife and detect alterations in their habitats. Utilizing the collated data, predictions can be made about ecosystem changes, and appropriate remedial measures can be initiated.
Similarly, computer vision can be deployed to scrutinize air and water quality. It can detect pollutant sources instantaneously and evaluate the severity of pollution. Based on these insights, necessary alterations can be implemented to prevent further environmental degradation.
Moreover, computer vision can be beneficial in detecting changes in land use and deforestation activities. This is particularly crucial in regions where illegal logging and mining operations occur. Authorities can leverage computer vision to track such activities and enforce necessary interventions.
In summary, computer vision can revolutionize environmental monitoring and conservation, providing real-time climate change data and aiding in devising strategies to minimize human-induced damage.
One of the vital applications of computer vision is in disaster response. Timely and effective response can mean the difference between life and death post-disaster. Computer vision can aid in locating areas requiring immediate help and assisting in search and rescue missions.
For example, drones powered by computer vision can relay live video footage of disaster-stricken areas to rescue teams, providing an aerial perspective. Computer vision algorithms can identify stranded individuals, falling debris, and other hazards in these feeds.
Floodwater and landslide risks
Computer vision can also identify environmental changes like floodwaters or landslides, enabling early warning systems to evacuate residents promptly. It can also track the progression of a disaster, helping direct assistance where needed.
In 2018, when Hurricane Harvey hit Texas, researchers used computer vision to help with disaster relief. The team looked at satellite images and computer vision algorithms to determine where the flooding was. This allowed emergency workers to figure out where to send supplies.
Assess the extent of the damage.
Another application of computer vision is in assessing the extent of disaster damage. Drones can capture images of damaged structures, and computer vision algorithms can analyze these images to ascertain the severity of the damage. This data can guide insurance companies and governmental agencies in determining the next steps.
Urban planning is critical in shaping and maintaining a city's infrastructure. Computer vision can prove instrumental in managing resources and future planning amidst rapid population growth and urbanization.
Data-driven decision making
Urban planners use computer vision to get information from diverse sources such as satellite images, sensors, and social media, facilitating informed decisions.. For example, computer vision can help with urban planning by giving information about traffic flow, air quality, people moving around, and other essential things.
The City of Boston's "Street Bump" app is a one-way computer vision in planning cities. This app sends information about road problems like potholes and cracks to the city's public works department using smartphone sensors. This helps the department determine how to fix and improve the city's infrastructure.
Tracking people's movement
Computer vision can analyze patterns in traffic flow from live camera feeds, identifying accident-prone zones or congestion.
This aids city planners in optimizing traffic light timings and rerouting traffic to alleviate congestion.
Heat Island Analysis
Computer vision plays a crucial role in urban heat island analysis by utilizing satellite images to identify areas with high temperatures and understanding the contributing factors, such as building density and vegetation coverage. This data aids urban planners in designing future developments that incorporate green spaces to mitigate the urban heat island effect and create more sustainable and livable cities.
Over recent years, the field of agriculture has been significantly enriched by the application of computer vision. This technology has been leveraged in a variety of innovative ways, providing transformative benefits.
For instance, the management of irrigation systems has seen a considerable boost in efficiency, thereby promoting water conservation. Furthermore, computer vision has played an instrumental role in enhancing crop yield, leading to an increase in agricultural productivity.
It has proven to be a reliable tool in the early detection of plant diseases and pest infestations, allowing for timely interventions and thus safeguarding crop health.
Computer vision is used in agriculture in several ways. One way is to help make farming smarter. Here, sensors and cameras track how crops grow and how healthy they are. This lets farmers find and fix problems quickly. Computer vision algorithms can look at pictures and videos of products to find patterns and spot signs of disease or pests. Farmers can act swiftly and do as minor damage as possible to their crops because of this early warning.
Computer vision is also integral to precision agriculture, where sensor data is used to maximize crop yield and minimize resource use. By analyzing data on soil quality, temperature, humidity, and more, computer vision algorithms can determine optimal growing conditions.
Computer vision can be utilized for more than just making crops produce more. It can also keep food from getting contaminated. Computer vision algorithms can examine pictures of crops and food to see any signs of contamination or spoilage. This lets farmers and food producers act quickly to stop the spread of illnesses caused by eating contaminated food.
Computer vision has made it easier for the government to do its job and work for the public interest. For example, the public and essential people are often invited to government agencies' events, meetings, and conferences. Essential, therefore, it's to monitor who comes to these kinds of events and ensure they are safe.
Adoption of face recognition
Face recognition systems that use computer vision to do these things can do them quickly and accurately. It ensures human faces can be recognized automatically and matched to a database of authorized staff or registered attendees. They can immediately see if someone shouldn't be there and tell the security team. This makes it a lot easier to decide who can get in.
Gather insights about people's moods.
Computer vision can also discover what people are doing and how they feel. For example, cameras that can read facial expressions can pick up on how the audience feels and what they do. Then, we can find out how interested they are in the event or specific topics by using this information. As a result, government agencies can improve public relations and contact more people with this information.
For example, the government of New Zealand has used computer vision technology to improve the image of its tourism industry. The government has set up a system at Auckland International Airport to take pictures of people entering the country. This is done so that the system can match their faces to the images on their passports. The photos are compared to a list of criminals, terrorists, and other people who are wanted.
This system facilitates the identification of dubious travelers for border guards, enhancing the safety of the borders. It has revolutionized perceptions about border crossing by simplifying the process.
Computer Vision is reshaping governmental operations, bolstering its effectiveness and efficiency. This technology is emerging as a pivotal game-changer in diverse domains, including federal-level cyber defense, traffic management, and agriculture. It empowers the government to tackle issues previously deemed insurmountable.
Cogent Infotech is leading the charge in identifying novel applications of computer vision to aid governmental objectives. Our team of seasoned professionals collaborates to tailor solutions that suit each client's unique requirements.
Explore our collection of insightful resources to understand more about how computer vision is transforming government operations.