Protecting corporate assets and information, and ensuring the safety of team members should be two of the highest priorities for any business. According to BusinessWire, the market value of investigation and security services will climb to $417.16 billion by 2025. However, minimizing losses remains challenging for security teams in many different business environments, including retail, fintech, transportation, and other industries, due to complex workflows and the increasing number of cyberattacks. Fortunately, thanks to the continuous development of computer vision technology, maintaining security can be more effective.
Understanding how computer vision works
Computer vision is a branch of 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 images to be analyzed. In business security, these photos are likely taken from surveillance cameras. The higher the image quality, the more accurate the results.
2. Data scientists train systems to identify certain objects in data. If the computer's machine learning algorithm detects a match, it marks that region of the image.
3. Then, the computer makes a decision based on what it sees, depending on how it has been trained to react.
This approach faces several challenges. Occasionally, objects seen through the camera may produce false positives. For example, a camera trained to identify weapons mounted on a person's belt might be confused with someone carrying a mobile phone. The accuracy of computer vision depends on camera quality, the amount of data used for training, and other variables. To fully leverage computer vision, businesses need to be aware of these challenges and mitigate their impact.
For example, facial recognition is a popular example of computer vision security. However, processing facial recognition data can place a heavy burden on network bandwidth. A potential solution to meet security requirements might be edge biometrics, where AI processing occurs on edge devices rather than in a centralized location. Therefore, before embarking on a computer vision implementation process, 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 that enhance security
Computer vision has numerous applications in security. Some examples include theft and fraud prevention, manufacturing defect detection, traffic accident detection, security assessment, and hazardous object detection. Let's take a closer look at each case.
Theft and fraud prevention
By using computer vision technology, losses from store theft can be better monitored and recorded. Businesses like Walmart are already using cameras with artificial intelligence to track theft. If a camera sees a customer putting items into their bag without scanning their luggage at self-checkout, a cashier will be called in for automated assistance.
Such a solution can be achieved by adding AI-powered cameras at checkout. When a customer scans a product at checkout, the camera captures the scanned items, and the system generates a total count and sends it to the integrated POS system. The POS system then compares the total number of scanned items with the number generated by the camera; if the numbers don't match, it sends a notification to store staff about a potential theft. This allows staff to respond quickly to potential negative events and prevent fraud.
Defect detection in manufacturing
At first glance, defect detection doesn't seem like a perfect fit for other security applications. However, automating the detection of defective items in a factory can help mitigate security issues. It can also help prevent vandalism and tampering. These systems can also help predict risks, enabling businesses to act on threats before it's too late.
Defect detection in manufacturing powered by machine learning algorithms allows for the identification of patterns in datasets and the detection of anomalies based on these patterns. This helps prevent human error with less time and effort, resulting in significant cost savings.
Traffic accident detection
Monitoring road accidents is crucial in certain situations, particularly in logistics, incident security, and traffic control. 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 can continuously monitor traffic to identify patterns that indicate potential accidents. If the system detects a potential hazard, it can alert the responsible party or execute a pre-programmed response to warn the driver.
Security assessment
Computer vision can be used to ensure workplace safety protocols are followed. For example, in back-office environments such as manufacturing, distribution, or retail, cameras can detect whether a pallet is lying flat on the floor or supported against a wall. Since the latter may be considered a safety hazard, a computer vision system can automatically flag the incident as a "near miss" and report the issue to supervisors for corrective action.
Hazardous 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 concealed due to environmental lighting, object pose, camera system perspective, occlusion, and other factors. While this technology may not be perfect yet, it can still be used to supplement and improve human safety work.
Summary – Implications of Computer Vision and Security
Businesses have diverse and unique security needs, which are often incompatible with one-size-fits-all solutions. Full automation may be effective in certain situations, such as detecting activity in specific areas or identifying defective items. However, for some businesses, a hybrid approach may be the best option, as computer vision can complement human operators. In any case, technology is constantly evolving, and businesses that want to effectively maintain security need to consider adopting these technologies to reduce losses, prevent incidents, and ensure the safety of their teams and customers.
