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

Computer vision is a discipline within artificial intelligence that aims to emulate 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 make conclusions from those observations. 

Computer vision is made possible by the following process:

There are several challenges to this approach. Occasionally objects seen through a camera may throw a false positive. For example, a camera trained to recognize a weapon holstered to a person’s belt might be confused by someone with a holstered cell phone. The accuracy of computer vision is dependent on the quality of the camera, the amount of data used for training, and other variables. To take full advantage of computer vision, businesses need to be aware of these challenges to mitigate their effects.

For example, facial recognition is a popular example of computer vision security. However, processing facial recognition data can create heavy loads on network bandwidth. A potential solution that maintains security needs might be edge biometrics, where AI processing occurs on edge devices instead of at a centralized location. So, before starting with the process of implementing computer vision you need to remember that each case is unique and it requires the involvement of experienced AI engineers to create the most effective solution. 

Computer vision use cases are numerous in security applications. A few examples include theft and fraud prevention, defect detection in manufacturing, traffic incident detection, safety assessment, and dangerous object detection. Let’s dive into each case in more detail.

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

Such a solution can be implemented by adding an AI-powered camera to checkouts. When a customer scans products at the checkout, the camera captures the scanned items and the system generates a total number of items and sends it to the integrated POS system. Then the POS system compares the total number of scanned items with the number generated by the camera and if the numbers don’t match, it sends a notification to the store employee about the potential theft. This enables employees to respond quickly to potentially negative incidents and prevent fraud.

At first glance, defect detection doesn’t exactly fit in with other security applications. However, automatically detecting defective items at the factory can help mitigate safety concerns. It can also help prevent sabotage and tampering. These systems can help predict risk as well, which allows businesses to take action on threats before it’s too late.

Defect detection in manufacturing powered by machine learning algorithms allows for finding patterns in a data set and detecting anomalies based on them. This helps prevent human error with less time and effort, resulting in significant cost savings.

Monitoring incidents that occur on the road is extremely important in several 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 available data and image streams from traffic cameras, such systems can continuously check the traffic to identify patterns that indicate a possible accident. If the system detects a potentially dangerous scenario, it can alert those responsible or implement pre-programmed responses to alert drivers.

Computer vision can be used to ensure safety protocols are enforced at the workplace. For example, in a manufacturing, distribution, or retail backroom environment, a camera can detect if a pallet is placed flat on the floor or is propped up on its side against a wall. Since the latter can be considered a safety hazard, the computer vision system can automatically flag the incident as a ‘near-miss,’ reporting the issue to a supervisor for correction. 

Systems equipped with computer vision technologies can be used to detect dangerous objects like weapons or other unauthorized items. This is a challenging application to implement because weapons may be easy to conceal due to the lighting in the environment, the pose of the subject, the perspective of the camera system, occlusion, and much more. Although the technology may not yet be perfect, it can still be used to supplement and improve security efforts alongside humans. 

Businesses have a variety of unique security needs that are often incompatible with a one-size-fits-all solution. Full automation may be effective for certain contexts, like detecting activity in a particular area or detecting defective items. However, a hybrid approach may be the best option for some businesses where computer vision can supplement human operators. Regardless, the technology is continuing to improve, and businesses that want to maintain security effectively need to consider adopting these technologies to reduce losses, prevent accidents, and ensure the safety of their teams and clients.