Feb 09 2021

How Computer Vision Is Helping Agencies See Their Mission Objectives More Clearly

Computer vision takes image processing a step further and provides agencies with deep and potentially critical insights.

Federal agencies are using artificial intelligence and machine learning to literally see the world differently. With computer vision, agencies can use AI-driven algorithms to automate processes involving photos or videos and derive deep insights through imagery.

Data is processed through neural networks at the edge and in the cloud, uncovering patterns that human beings may not be able to see or understand.

LEARN: Find out how to bring federal workers into the conversation around emerging technologies.

Why Is Computer Vision so Important?

Deriving real meaning from imagery has always been a challenge. The process typically required many hours of human involvement.

Computer vision provides the ability to automate and scale image and video evaluation in near real time in a variety of use cases. For example, office managers can use video footage to immediately and accurately gauge whether or not federal employees are properly following coronavirus pandemic protocols for social distancing. Physicians can make better decisions on how to treat patients based on insights gleaned from computerized imagery that is automatically processed and analyzed. Government infrastructures can be quickly analyzed for structural weaknesses using drone footage.

Computer vision can also help federal law enforcement agencies move beyond traditional image processing, allowing them to leverage photos and video footage in even more meaningful ways. For instance, today the FBI is using computer vision to perform wide-ranging web searches to root out child pornography and quickly identify criminals. The level of work being done at this rate would be impossible if left to manual resources. With computer vision, these criminals can more quickly and accurately be identified and apprehended, and their sites shut down.

Computer Vision Helps Put Imagery in Context

Computer vision can also place images into greater context. One example is the use of “scene intelligence” to gain detailed and accurate insights into a particular environment or person.

Through scene intelligence, a computer vision system can alert users to anomalies that are occurring in a specific place at a certain time. For example, a surveillance camera in an airport could detect a passenger exhibiting an odd gait, which could indicate that person is carrying some kind of weapon. This might be unnoticeable to the naked eye, but the system can detect it by correlating the image data with algorithms that define a normal stride pattern.

What happens if the AI erroneously identifies a person with a disability as a threat? Bias could occur if the data provided to the AI only represents a small subset of available information. In the same way that humans are less prone to bias upon experiencing the diverse world around them, feeding the AI system large, varied and well-represented training data sets will mitigate bias by helping the system learn from multiple data points.

READ MORE: What are the key differences between artificial intelligence and machine learning?

How Sensors and AI Can Aid Agencies

If AI is the brain, computer vision is the eyes. There are many other ways humans absorb information that are currently in the process of being digitalized, all with their own unique benefits.

Technologies that use depth-sensing cameras can mimic human touch, replacing the need to physically tap a screen and resulting in a truly touchless experience — critical during a pandemic. Sensors can detect unusual or suspicious smells that the human nose may not sense or recognize, which can prove important in the identification of dangerous gases.

Still, computers will never replace people. Indeed, AI without humans to interpret and learn from its data is truly a black box. Agencies need data scientists to make sense of the findings, but the real beneficiaries, from a personnel perspective, are the people whose jobs are improved by the insights and recommendations that AI provides.

An automated AI system can help an employee build a better solution by identifying any mistakes the person may make, or by offering solutions to potential problems. Projects like the OpenVINO toolkit are democratizing the creation and use of AI solutions, making the technologies available to those who want to create their own vision applications.

Those applications are going to power the next generation of AI-driven insights. For now, agencies have the ability to see more clearly and accurately through computer vision, which allows them to visualize and address both challenges and opportunities.

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