Tech

How Technology Reduces Theft in AI Self-checkout Systems

Have you ever found yourself standing in a long line at the grocery store, impatiently waiting to pay for your items? Maybe you’ve even experienced that heart-stopping moment when you realize you accidentally forgot to scan an item and now have to go back and pay for it. Fortunately, with the rise of AI self-checkout systems, these inconveniences are becoming less common. But what about theft? How do retailers prevent customers from walking away without paying for their purchases? The answer lies in technology – read on to discover how advanced algorithms and computer vision are reducing theft in AI self-checkout systems!

Published

2 years ago

June 14, 2023

Adva

In the evolving landscape of retail, self-checkout systems have become increasingly popular due to their convenience and efficiency. However, concerns regarding theft and fraudulent activities have often been associated with these automated systems. To address these challenges, modern self-checkout systems are leveraging the power of AI technology to enhance security measures and reduce theft. In this article, we will explore how AI technology is playing a crucial role in enhancing security and deterring theft in modern self-checkout systems.

Accurate Item Recognition:

One of the key features of AI technology in self-checkout systems is accurate item recognition. By employing computer vision algorithms and machine learning models, these systems can recognize and verify items being scanned by customers. The AI algorithms analyze product images or barcodes to ensure that the scanned item matches the one in the system’s database. If there is a discrepancy or an unexpected item is detected, the system prompts the customer for verification or alerts store staff. This validation process acts as a deterrent to intentional theft or scanning errors.

Real-Time Monitoring and Alert Generation:

AI self-checkout systems are equipped with advanced surveillance capabilities. Integrated cameras strategically placed in the self-checkout area capture real-time video footage. AI algorithms analyze this footage to monitor customer behavior and detect any suspicious activities. For instance, the system can identify instances of product switching, incorrect scanning, or attempts to bypass the payment process. When such anomalies are detected, the system generates alerts or notifies store staff for immediate intervention. This proactive monitoring helps prevent theft and provides valuable evidence for investigations if necessary.

Behavioral Analysis and Anomaly Detection:

AI technology enables self-checkout systems to analyze customer behavior and identify patterns or anomalies that may indicate potential theft. By monitoring scanning patterns, transaction times, and item weights, the system establishes baseline behavior for each customer. Any deviations from the established patterns can raise suspicion and trigger alerts. For example, if a customer consistently scans items too quickly or fails to scan certain items, the system can flag these behaviors as potential theft indicators. Store staff can then step in to resolve the issue, preventing loss and maintaining a secure shopping environment.

self-checkout systems

Integration with Loss Prevention Systems:

AI self-checkout shopping carts and their AI systems can be seamlessly integrated with existing loss prevention systems within the store. This integration allows for real-time data sharing and coordination between different security measures. For instance, the self-checkout system can communicate with electronic article surveillance (EAS) tags or RFID systems to cross-verify scanned items with the products leaving the store. If an item is not properly scanned or removed from the premises without authorization, an alarm can be triggered. This integration enhances the overall security ecosystem and acts as a powerful deterrent against theft.

Staff Monitoring and Training:

While AI technology plays a crucial role in reducing theft, the presence of attentive store staff remains essential. In addition to the automated security measures, store employees are responsible for monitoring the self-checkout area and addressing any suspicious activities promptly. They can assist customers, verify age-restricted purchases, and provide guidance on the correct usage of the self-checkout systems. Proper staff training on theft prevention strategies and the effective utilization of AI technology further strengthens the security measures.

AI technology is revolutionizing the security landscape of modern self-checkout systems. Accurate item recognition, real-time monitoring, behavioral analysis, and integration with loss prevention systems are just a few ways in which AI is reducing theft and enhancing security. By leveraging the power of AI, retailers can offer customers a seamless and secure self-checkout experience while mitigating the risks associated with theft and fraud.

Frequently Asked Questions:

How does AI technology in self-checkout systems prevent shoplifting?

AI technology in self-checkout systems employs accurate item recognition and real-time monitoring to detect suspicious activities. It can identify product switching, incorrect scanning, or attempts to bypass payment. When anomalies are detected, alerts are generated, and store staff can intervene, preventing shoplifting and maintaining a secure shopping environment.

Can AI self-checkout systems differentiate between intentional theft and innocent scanning errors?

Yes, AI self-checkout systems are designed to differentiate between intentional theft and innocent scanning errors. The systems utilize algorithms that analyze various factors, such as customer behavior patterns and consistency, to determine when a potential theft is occurring. This helps minimize false alarms and ensures that customers are not wrongly accused of theft due to accidental mistakes.

How effective are AI self-checkout systems in reducing theft compared to traditional cashier-assisted checkout?

AI self-checkout systems have proven to be effective in reducing theft when implemented properly. The combination of accurate item recognition, real-time monitoring, and proactive alerts significantly deters theft. While traditional cashier-assisted checkout also has security measures in place, AI technology adds an extra layer of protection by continuously monitoring and analyzing customer behavior.

What happens when the AI self-checkout system detects a potential theft or fraudulent activity?

When an AI self-checkout system detects a potential theft or fraudulent activity, it typically generates an alert. Store staff members are immediately notified and can respond accordingly. They may observe the transaction, provide assistance to the customer, or intervene to prevent further loss. The presence of staff ensures a swift and appropriate response to maintain security and address any issues that arise.

Are AI self-checkout systems prone to false alarms or accusing innocent customers of theft?

While AI self-checkout systems are designed to minimize false alarms, there is always a possibility of errors. Factors such as scanning errors, technical glitches, or unusual customer behavior patterns can occasionally trigger false alarms. However, these systems are continuously improved and fine-tuned to minimize such occurrences. Additionally, store staff members play a vital role in assessing the situation, intervening, and ensuring that innocent customers are not wrongly accused.

Business Solutions

Geneo Glam: Skin Firming Treatment for Radiant, Youthful Skin

Geneo Glam is the ultimate skin firming treatment designed to restore elasticity, enhance radiance, and leave you with a glowing, youthful complexion.

Published

7 hours ago

May 9, 2025

Marks Strand

The Geneo Glam skin firming treatment is a luxurious, non-invasive facial that revitalizes the skin by improving firmness, elasticity, and hydration. Using advanced OxyPod technology, this treatment delivers a unique combination of exfoliation, oxygenation, and infusion of active ingredients to help the skin look smoother, tighter, and more radiant.

Key Benefits

Firms and Hydrates
The treatment boosts collagen and elastin production, helping skin feel firmer and more supple.
Improves Elasticity
Increases the skin’s resilience and reduces the appearance of fine lines and wrinkles.
Prevents Collagen Breakdown
Helps preserve the skin’s youthful structure by protecting existing collagen and supporting healthy cell function.

Powerful Natural Ingredients

24K Gold Particles
Stimulate collagen production, protect skin fibers, and encourage cell renewal for a firmer, lifted appearance.
Silk Amino Acids
Strengthen the skin barrier, lock in moisture, and support collagen synthesis to reduce visible signs of aging.
Carnosine Peptides
Help protect the skin from sugar-related damage (glycation), delay cellular aging, and extend the life of skin cells.
Copper
An antioxidant and anti-inflammatory that supports collagen development, smooths fine lines, and helps with skin regeneration.

How the Treatment Works

Exfoliation and Oxygenation
The Geneo Glam OxyPod is activated with a Primer Gel, gently exfoliating the skin and triggering a natural oxygenation process that increases blood flow and enhances skin vitality.
Infusion of Actives
Active ingredients such as gold particles, peptides, and amino acids are infused deep into the skin to firm and rejuvenate.
Hydration and Nourishment
A final serum containing hyaluronic acid, rosehip oil, and marula oil hydrates and soothes the skin, leaving it soft and glowing.

Who Should Try Geneo Glam?

This treatment is ideal for people who want to:

Reduce fine lines and early signs of aging
Firm and tighten sagging skin
Restore hydration and improve skin tone

Geneo Glam offers a refreshing way to firm, lift, and hydrate your skin—leaving you with a youthful glow and smooth, resilient skin. It’s a perfect solution for anyone seeking visible results without invasive procedures or downtime.

Business Solutions

H.265 miniature UAV encoders: A comprehensive Overview

H.265 miniature UAV encoders revolutionize aerial technology with advanced video compression, ensuring high efficiency and superior performance for modern UAV systems.

Published

1 day ago

May 8, 2025

Adva

As the demand for high-quality, real-time video transmission from unmanned aerial vehicles (UAVs) continues to rise in both military and commercial applications, the need for efficient, compact video encoding solutions has become paramount. H.265 miniature UAV encoders represent a significant advancement in this space, providing robust video compression in a small, lightweight package ideal for drones with stringent size, weight, and power (SWaP) constraints. Leveraging the power of High Efficiency Video Coding (HEVC), also known as H.265, these encoders allow UAVs to deliver high-resolution video over constrained data links, enhancing situational awareness and operational effectiveness without overwhelming available bandwidth.

H.265 is a video compression standard that succeeds H.264/AVC and offers approximately double the data compression ratio at the same video quality level. This efficiency is particularly beneficial for UAV applications, where bandwidth and power availability are limited, especially during beyond-line-of-sight (BLOS) missions or in contested environments. With H.265 encoders, UAVs can stream 1080p or even 4K encoder video in real time while consuming significantly less data than older standards. This is critical for operations such as intelligence, surveillance, and reconnaissance (ISR), where maintaining video clarity over long distances or through relay networks is essential for accurate decision-making.

Miniature H.265 UAV encoders are engineered to operate under harsh environmental conditions while maintaining optimal performance. These devices are typically ruggedized, featuring extended temperature ranges, shock resistance, and electromagnetic shielding to ensure reliable operation in military or field environments. Despite their small size—often no larger than a deck of cards—they include advanced features such as low-latency encoding, dynamic bitrate control, encryption, and support for multiple streaming protocols including RTSP, RTP, and MPEG-TS. This allows them to integrate seamlessly into existing command-and-control infrastructure and support a variety of end-user applications, from real-time ground monitoring to autonomous navigation and object tracking.

H.265 miniature UAV encoders

The integration of H.265 encoders into small UAVs has significantly expanded the capability of tactical drone systems. For example, military units can deploy hand-launched drones equipped with these encoders to provide persistent ISR coverage over a battlefield, transmitting clear, actionable video intelligence back to command centers in near real time. Law enforcement agencies and border security forces also benefit from these technologies, using UAVs to monitor large or remote areas with minimal personnel. In disaster response scenarios, such encoders enable drones to deliver live aerial assessments of affected regions, helping responders prioritize actions and coordinate relief efforts efficiently.

Beyond video transmission, modern H.265 UAV encoders are increasingly integrated with onboard artificial intelligence modules that enable edge processing. This allows UAVs to perform real-time object recognition, motion detection, and scene analysis directly within the encoder, reducing the need to send raw data to centralized systems for processing. Such capabilities are crucial in time-sensitive missions where latency can affect outcomes, such as tracking moving targets or identifying threats in complex terrain.

Despite their many advantages, the deployment of H.265 miniature encoders does come with some technical considerations. The encoding process, while more efficient than previous standards, requires higher computational resources. Manufacturers must therefore strike a careful balance between processing power, thermal management, and energy consumption. Additionally, the compatibility of H.265 streams with legacy systems remains a factor, as not all ground stations or video players natively support HEVC decoding without updates or specialized software.

Manufacturers of H.265 miniature UAV encoders include companies such as IMT Vislink, Soliton Systems, Haivision, and VITEC, all of which provide solutions tailored to UAV and robotics applications. These encoders are often modular, allowing integrators to select configurations based on mission requirements, payload limitations, and transmission needs. As the ecosystem of compact, high-efficiency video systems grows, continued innovation in low-power silicon and AI integration is expected to drive the next wave of capability enhancements in this field.

In the evolving landscape of drone technology, H.265 miniature UAV encoders stand out as a critical enabler of high-performance video transmission. By combining advanced compression with minimal SWaP impact, these systems provide UAV operators with the tools to observe, analyze, and act with unprecedented precision and clarity—no matter how small the platform or how demanding the environment.

Business Solutions

IEEE 802.11p and V2X Communication: Enabling Smarter, Safer Roads

IEEE 802.11p revolutionizes V2X communication, driving smarter, safer roads through advanced vehicle connectivity. This cutting-edge technology enhances transportation systems, enabling intelligent and secure interactions for a safer future.

Published

2 days ago

May 7, 2025

Adva

Modern vehicles are no longer isolated machines; they are becoming intelligent, connected nodes within a larger transportation ecosystem. At the heart of this transformation is Vehicle-to-Everything (V2X) communication, which enables cars to talk to each other and to the infrastructure around them. One of the first and most influential technologies developed to support V2X is the IEEE 802.11p standard—a wireless standard specifically tailored for vehicular environments.

What is IEEE 802.11p?

IEEE 802.11p is an amendment to the IEEE 802.11 standard (commonly known as Wi-Fi), designed to enable wireless access in vehicular environments. It was approved in 2010 and forms the basis for Dedicated Short-Range Communications (DSRC).

Key Characteristics of 802.11p:

Frequency Band: Operates in the 5.9 GHz band reserved for Intelligent Transportation Systems (ITS).
Low Latency: Optimized for fast, real-time communication necessary for safety-critical applications.
Range: Effective communication range of up to 1 kilometer, suitable for high-speed vehicle interaction.
Decentralized Architecture: Enables direct communication (V2V and V2I) without the need for cellular or network infrastructure.
Robustness: Handles high-speed mobility and rapidly changing topologies typical of vehicular environments.

Role of 802.11p in V2X Communication

V2X (Vehicle-to-Everything) is a broader term encompassing various communication paradigms, including:

V2V (Vehicle-to-Vehicle)
V2I (Vehicle-to-Infrastructure)
V2P (Vehicle-to-Pedestrian)
V2N (Vehicle-to-Network)
V2C (Vehicle-to-Cloud)

802.11p primarily supports V2V and V2I communications, forming the backbone of DSRC-based V2X implementations. Its low latency and direct communication capabilities make it ideal for applications such as:

Forward collision warnings
Intersection movement assist
Emergency electronic brake lights
Lane change warnings

Comparison with Cellular V2X (C-V2X)

As V2X technology has evolved, C-V2X (based on LTE and 5G standards) has emerged as a strong alternative to 802.11p. Here’s how they compare:

Feature	IEEE 802.11p (DSRC)	C-V2X (LTE/5G)
Latency	~10 ms	~5–10 ms (LTE), <5 ms (5G)
Coverage	Short-range, direct	Short + long-range via network
Deployment	Mature, field-tested	Growing, especially with 5G
Infrastructure	Minimal (no cellular needed)	Requires cellular networks (for V2N/V2C)
Interoperability	Limited with C-V2X	Newer versions support dual-mode

Adoption and Use Cases

Global Deployment:

United States: Initially favored DSRC based on 802.11p, though recent FCC rulings have shifted focus toward C-V2X.
Europe: ETSI has defined ITS-G5, a protocol stack based on 802.11p.
Japan and South Korea: Active use of DSRC for tolling and traffic safety.

Real-World Applications:

Collision avoidance systems
Smart intersections
Road hazard notifications
Platooning for commercial vehicles
Public transport priority systems

Advantages of 802.11p

Mature and Proven: Used in numerous pilot programs and early deployments.
Fast Time to Communication: No need for handshake protocols; devices can communicate almost instantly.
No Subscription Costs: Operates independently of cellular networks.

Limitations and Challenges

Scalability: In high-density traffic, packet collisions may reduce reliability.
Spectrum Allocation: Regulatory changes in some countries have limited the bandwidth available to DSRC.
Limited Ecosystem Growth: Many automakers and countries are shifting investment to C-V2X and 5G-based platforms.

Future Outlook

While 802.11p has laid the foundation for V2X communication, the industry is gradually pivoting toward more advanced and scalable technologies such as 5G NR-V2X. However, 802.11p remains relevant in regions where DSRC infrastructure is already deployed and continues to serve as a dependable option for immediate, low-latency vehicular communication.

Hybrid Solutions:

Some industry players are exploring dual-mode V2X devices that support both 802.11p and C-V2X, ensuring backward compatibility and smoother transitions.

IEEE 802.11p has played a pivotal role in launching the era of connected vehicles, offering reliable, low-latency communication tailored for high-speed mobility. While newer technologies like C-V2X and 5G are beginning to dominate the roadmap, 802.11p’s contributions remain foundational in the evolution of V2X systems. As the automotive industry moves forward, a mix of technologies, including legacy support for 802.11p, will ensure that safety, efficiency, and connectivity continue to advance on roads around the world.