Improving the security of business systems with computer vision | Techno Glob

Improving the security of business systems with computer vision
Illustration: © IoT for All

Protecting business assets and information and ensuring the safety of team members should be two of the top priorities of any business. According to BusinessWire, the value of the investigation and security services market will grow to $417.16 billion by 2025. But it’s still challenging for security teams to mitigate losses in various business environments, including retail, fintech, transportation and other industries, because of complex workflows and the increasing number of cyber attacks. Fortunately, thanks to evolving computer vision technology, maintaining security can be more efficient.

Understanding how computer vision works

Computer vision is a discipline in artificial intelligence that aims to emulate how humans observe and perceive the visual world. This technology has many applications. Training computers to understand how to recognize objects and draw conclusions from those observations requires data.

Computer vision is made possible by the following processes:

  1. A computer must have access to the images to perform the analysis. In business security, this is likely to be taken from a surveillance camera. The higher the image quality, the more accurate the results.
  2. Data scientists train systems to recognize specific objects in data. If the computer’s machine learning algorithm finds a match, it flags a region of the image.
  3. The computer then makes decisions based on what it sees, depending on how it has been trained to respond.

There are several challenges to this approach. Occasionally objects seen by the camera may throw false positives. For example, a camera trained to recognize a weapon attached to a person’s belt could confuse someone with a holstered cell phone. The accuracy of computer vision depends on the quality of the camera, the amount of data used for training, and other variables. To take full advantage of computer vision, businesses must be aware of these challenges to minimize their impact.

For example, facial recognition is a popular example of computer vision security. However, processing facial recognition data can place a heavy load on network bandwidth. A possible solution to address security needs could be edge biometrics, where AI processes take place on edge devices rather than in a centralized location. Therefore, before starting the process of implementing computer vision you need to remember that each case is unique and requires the involvement of experienced AI engineers to create the most effective solution.

Business cases for computer vision to enhance security

Use cases of computer vision in security applications are numerous. Some examples include theft and fraud prevention, product defect detection, transportation incident detection, security assessment, and dangerous goods detection. Let’s go into each case in more detail.

Prevention of theft and fraud

Shrinkage from shoplifting can be better monitored and recorded using computer vision techniques. Businesses like Walmart are already using cameras with artificial intelligence to track theft. If a camera sees a guest place an item in their bag without scanning it during self-checkout, an attendant is automatically called to assist.

Such a solution could be implemented by adding an AI-powered camera to the checkout. When a customer scans products at the checkout, a camera captures the scanned items and the system creates a total item count and sends it to the integrated POS system. The POS system then compares the total number of items scanned with the number generated by the camera, and if the numbers don’t match, it sends an alert to store staff about possible theft. This enables employees to respond quickly to potential negative events and prevent fraud.

Finding product defects

At first glance, fault detection does not fit neatly into other security applications. However, automatically detecting factory-defective items can help reduce security concerns. It can also help prevent vandalism and tampering. These systems can also help predict risk, allowing businesses to act on threats before it’s too late.

Manufacturing defect detection powered by machine learning algorithms allows finding patterns in data sets and detecting anomalies based on them. It helps avoid human error with less time and effort, resulting in significant cost savings.

Traffic incident detection

Monitoring road events is extremely important in many contexts, especially logistics, event security, traffic control and more. Computer vision-enabled cameras can detect crashes, identify suspicious moving and parked vehicles, and automatically respond to potential threats or objects of interest.

By learning from the data and image streams available from traffic cameras, such systems can continuously monitor traffic to identify patterns that indicate potential accidents. If the system detects a potentially dangerous situation, it can alert responsible persons or implement pre-programmed responses to drivers.

Safety assessment

Computer vision can be used to ensure that workplace safety protocols are implemented. For example, in a manufacturing, distribution or retail backroom environment, a camera can detect whether a pallet is placed flat on the floor or placed on its side against a wall. The latter can be considered a safety hazard, with computer vision systems automatically flagging the incident as a ‘near-miss’, notifying a supervisor to correct the problem.

Dangerous object detection

Systems equipped with computer vision technology can be used to detect dangerous items such as weapons or other unauthorized items. This is a challenging application to implement because ambient lighting, subject pose, camera system perspective, obstacles, and more can make it easy to conceal weapons. Although the technology is not yet perfect, it can be used to complement and improve security efforts alongside humans.

Wrapping Up – Computer Vision and Security Implications

Businesses have a variety of unique security needs that are often incompatible with a one-size-fits-all solution. Full automation can be effective for specific contexts, such as detecting activity in a specific area or detecting defective items. However, a hybrid approach may be the best option for some businesses where computer vision can complement human operators. Regardless, technology is constantly improving and businesses that want to maintain security effectively need to consider adopting this technology to minimize damage, prevent accidents, and ensure the safety of their teams and customers.

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