Improving business system security using computer vision

Protecting corporate assets and information and keeping team members safe should be the two highest priorities for any business. According to BusinessWire, the investigation and security services market will climb to a value of $417.16 billion by 2025. However, due to complex workflows and an increasing number of cyberattacks, it remains challenging for security teams to minimize losses in many different business environments, including retail, fintech, transportation and other industries. Fortunately, maintaining security can be more effective thanks to evolving computer vision technology.

Understand how computer vision works

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

Computer vision is achieved through the following process:

1. The computer must have access to the image to be analyzed. In terms of business security, the photos were most likely taken from a surveillance camera. The higher the image quality, the more accurate the results.

2. Data scientists train the system to recognize certain objects in the data. If the computer's machine learning algorithm detects a match, it flags that area of ​​the image.

3. The computer then makes a decision based on what it sees, depending on how it was trained to react.

This approach faces several challenges. Occasionally, objects seen through the camera may emit false positives. For example, a camera trained to identify a weapon carried on a person's belt could be confused by a person carrying a 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 get the most out of computer vision, businesses need to be aware of these challenges to mitigate their impact.

For example, facial recognition is a popular example of computer vision security. However, processing facial recognition data places a heavy burden on network bandwidth. One potential solution to maintaining security needs could be edge biometrics, where AI processing occurs 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.

Computer Vision Business Cases for Enhanced Security

There are many use cases for computer vision in security applications. Some examples include theft and fraud prevention, manufacturing defect detection, traffic accident detection, safety assessment and hazardous object detection. Let's look at each case in more detail.

Theft and Fraud Prevention

Loss from store theft can be better monitored and recorded through the use of computer vision technology. Businesses like Walmart are already using cameras with artificial intelligence to track theft. If the camera sees a guest putting items into their bag without scanning it at self-checkout, an attendant will be called to automatically assist.

Such a solution could be achieved by adding AI-powered cameras at checkout. When a customer scans a product at checkout, a camera captures the scanned items and the system generates a total quantity of items and sends them to the integrated POS system. The POS system then compares the total number of items scanned to the number generated by the camera, and if the numbers do not match, a notification of the potential theft is sent to a store employee. This enables employees to quickly respond to potential negative events and prevent fraud.

Defect Detection in Manufacturing

At first glance, defect detection does not exactly fit into other safety applications. However, automating the detection of defective items at the factory can help mitigate safety concerns. It can also help prevent vandalism and tampering. These systems can also help predict risks, which enables 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 these patterns. This helps prevent human errors with less time and effort, resulting in significant cost savings.

Traffic Accident Detection

Monitoring accidents that occur on the road is very important in certain situations, especially logistics, event security, traffic control, etc. Computer vision-enabled cameras can detect collisions, identify suspicious moving and parked vehicles, and automatically react to potential threats or objects of interest.

By learning from available data and image streams from traffic cameras, such a system could continuously examine traffic to identify patterns that indicate a possible accident. If the system detects a potentially dangerous situation, it can alert the responsible person or perform a pre-programmed response to alert the driver.

Safety Assessment

Computer vision can be used to ensure workplaces are enforcing safety protocols. For example, in a back-office environment in manufacturing, distribution or retail, cameras could detect whether a pallet is lying flat on the floor or supported on its side against a wall. Since the latter could be considered a safety hazard, a computer vision system could automatically flag the incident as a "near miss" and report the problem to a supervisor for correction.

Dangerous Object Detection

Systems equipped with computer vision technology can be used to detect dangerous objects, such as weapons or other unauthorized items. This is a challenging application because weapons can be easily hidden due to lighting in the environment, pose of the subject, perspective of the camera system, occlusion, etc. While the technology may not be perfect yet, it can still be used to complement and improve human security efforts.

Summary - Computer Vision and Security Implications

Enterprises have a variety of unique security needs that are often incompatible with one-size-fits-all solutions. Full automation may be effective in certain situations, such as detecting activity in a specific area or detecting defective items. However, for some businesses, a hybrid approach may be the best option, as computer vision can complement human operators. Regardless, technology is still improving, and businesses that want to maintain security effectively need to consider adopting these technologies to reduce losses, prevent incidents, and keep their teams and customers safe. ​

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