25 Ways Companies Leverage AI Video Analytics in 2024

With millions of cameras dotted across retail outlets, transportation centers, office complexes, venues, and nearly every industrial center across the globe, video analytics is a treasure trove of valuable data that can significantly impact enterprises across industries.   

What is the difference between AI video analytics and computer vision? 

Computer vision (or Vision AI) is the overarching field of AI that deals with how computers can "see" and understand the visual world. This includes tasks like image and video analysis, object recognition, and facial recognition. While all computer vision allows you to detect people, objects, and events in real-time, video analytics is a specific function within the broader field of computer vision. 

What is video analytics?

AI Video analytics is a specific application of computer vision. It focuses on analyzing video footage to extract meaningful information. It uses the tools from computer vision to find things like people, objects, or actions in videos. Imagine a security guard watching hours of footage – video analytics can do that, but much faster and more effectively, looking for things like people, objects, or specific actions.

All AI video analytics software uses computer vision, but not all computer vision is video analytics because real-time computer vision can also be used for analyzing still images.

How are Companies Leveraging AI Video Analytics in 2024?

AI video analytics is revolutionizing a broad spectrum of industries by delivering advanced solutions tailored to specific operational needs. For any business whose operations include repeatable processes or stretch across areas that are difficult to monitor at all times, AI-based video analytics can provide unprecedented visibility in real-time. 

Nearly all industries use cameras for security purposes but much of that footage is only reviewed if an incident occurs (or is often never reviewed!). Real-time video analytics leverages existing cameras turning video footage into unprecedented visual insights. 

Below is a list of 24 use cases across a variety of industries where AI video analytics is making a significant impact.

Smart Manufacturing

• Most manufacturing plants are large, use multiple machines, employ many workers, and now, more and more, robots. Monitoring these facilities at all times is difficult. 
• Most manufacturing plants have repeatable processes that could be refined with real-time visual insights. 

1. Anomaly and Defect Detection: Defects can have a compounding effect on overall productivity. Defects can stop assembly lines, damage machinery, and create extra work to fix the defect. This lost productivity has been estimate to cost employers “$1.8 trillion every year.” Vision AI can identify irregularities and detect defects in products or machinery, in real-time, to prevent downtime and ensure quality. Immediate alerts can be sent to managers to address defects and anomalies as they are detected, minimizing costly disruptions. 

2. Process Optimization: Manufacturing often involves several, sometimes intricate processes stretched throughout a sprawling facility. Managers may know bottlenecks occur during a certain process, but pinpointing exactly where can be difficult – especially if there is limited human visibility. 

Using computer vision, manufacturers could track the time it takes to complete the process. Additionally, it can detect workers across the production line to determine issues like overcrowding or it can detect certain objects (i.e., products, a piece of equipment, or a machine). Whatever person, object, or event manufacturers want to monitor, computer vision can do it, in real-time to uncover hidden inefficiencies, optimize machine utilization, and reduce waste with 24/7. With the unprecedented insights garnered from AI video analysis, enterprises gain a greater understanding of processes to make improvements that will transform operations.

3. Safety Monitoring: Most manufacturing facilities use a variety of machines, materials, and increasingly more robots. Spinning parts, razor-sharp edges, autonomous robots, and scorching surfaces create an environment ripe for injuries and accidents. Vision AI can leave no corner unseen, constantly ensuring a watchful eye on critical areas. Track PPE compliance and identify risky machine operations – all before an incident disrupts production flow. Instant alerts allow managers to intervene, transforming the manufacturing floor from a gamble to a well-managed operation.

4. Heatmapping and traffic analytics: Employees, deliveries, materials, and equipment all vie for space in large manufacturing facilities. Optimizing this complex dance can feel like conducting an orchestra blindfolded. Limited visibility, wasted movement, congested areas, and inefficient resource allocation drain productivity and bottom lines. Video analytics systems can deliver heatmaps and foot traffic analytics to identify bottlenecks, optimize material flow, and empower manufacturers with actionable insights. 

Smart Mining

5. Health and Safety: Mining is dangerous due to a variety of factors including human error, equipment failures, unstable geology, explosives, and other safety concerns. Noncompliance with safety protocols, fatigue, poor maintenance, rock falls, floods, and electrical hazards, make ensuring miner safety a top priority. AI video analytics can identify and track PPE usage, monitor hazardous areas, track all traffic movements (underground, pit, hauler roads, etc.) to assist in collision avoidance, and even detect signs of fire or rock fall that could indicate fire or landslides. This type of round-the-clock, real-time surveillance is only available through AI video analytics. 

6. Productivity: Equipment downtime, supply chain disruptions, labor shortages, and inefficient operational practices all contribute to productivity losses in mining. By adopting AI video analytics, mines can get a clearer picture of their processes to better address productivity issues. For example, vision AI can detect blockages in crushers, broken teething in ground engaging tools, empty or damaged conveyor belts, idle pumps, and other issues that cause downtime. AI video analytics can monitor hoist capacity, cage activity, and any other equipment or process to enhance operational efficiency. 

7. Processing: Processing metals is complex. The ore must be sorted, crushed, analyzed, and transported requiring extensive use of various crushers, sorters, belts, transport vehicles, Ground Engaging Tools, and chemicals. If any of these machines or tools break down, the downtime can result in significant losses. AI video analytics can detect jammed crushers, monitor belts for material flow, spills, tears, and other damages, and even detect particle size. This advanced technology can also send immediate alerts when machines or equipment break to minimize costly downtime. 

8. Logistics: Logistics is an integral part of mining but can also be costly. The diesel needed to keep haul trucks operating typically accounts for 20-30% of total operating expenses. Underfilled and overfilled trucks can both contribute to hidden inefficiencies and safety hazards.  Real-time AI video analytics help operators optimize truck payloads (and thus fuel economy), monitor traffic flow for accident prevention, track hazardous materials for safety, and provide inventory control to minimize delays. 

Smart Energy

Similarly, the energy sector stands to gain significant advantages from AI video analytics to ensure safety, improve efficiency, and contribute to a more sustainable future.

9. Wind Turbine Farm Monitoring: Wind turbines are exposed to a variety of environmental conditions – wind, rain, and lightning – that accelerate their wear and tear. Expensive inspections by human crews battling harsh conditions, risk injury and downtime. Real-time automated monitoring with video analytics can pinpoint issues like rust or debris, reducing the need for risky climbs and blind inspections and minimizing costly slowdowns.

10. Solar Farm Monitoring: Keeping solar farms operating at peak efficiency can be a logistical nightmare thanks to remote locations, harsh weather, and ever-growing vegetation. AI Video analytics can identify defects and anomalies instantly to streamline maintenance and minimize downtime.

11. Analog Controls Recognition: Monitoring energy consumption and processes with analog controls is tedious, time-consuming, and prone to human error. A single missed reading can mean wasted energy, inefficient production, and even safety hazards. With AI-powered video analytics, you can track pressure gauges, temperature dials, and warning lights – to create an accurate digital stream of data, eliminating human error and missed readings. Improve efficiency, ensure accuracy, and proactively address potential hazards. 

12. Automated Intrusion Detection: Manually monitoring energy facilities is a losing battle thanks to hazardous areas, sensitive equipment, and the constant risk of unauthorized activity. But with AI video analytics, companies can automate intrusion detection to get instant alerts about after-hours movement and unauthorized activity around the perimeter of facilities. 

Computer vision powering a heatmap of a factory floor

Smart Warehousing and Logistics

The warehousing and logistics industries are well-suited to real-time visual data as they thrive on accurate and detailed information about inventory, shipments and deliveries, and safety compliance. 

13. Pallet and Inventory Tracking: Complex customs regulations, variable lead times, and mountains of inventory create a breeding ground for bottlenecks and inefficiencies in warehousing and logistics. Lost time searching for misplaced pallets and the constant threat of theft erode bottom lines and frustrate customers. AI video analytics can detect pallets in seconds, send instant alerts about damaged or stolen goods, and provide actionable, real-time data to minimize delays, streamline operations, and elevate customer service. 

14. Volumetric Space Optimizer: Trucking inefficiencies hemorrhage billions of dollars annually from the industry. Every half-empty truck is a wasted opportunity, a drain on profits disguised as a delivery vehicle. AI-powered video solutions deliver real-time insights into capacity and usage, customized for your specific needs. Reduce unused space and optimize deliveries to drive more revenue.

15. Arrival, Delivery, and Dispatch Tracking: As supply chain complexity grows, so does the potential for inaccurate or missing information about arrivals and deliveries, leading to costly delays. Vision AI provides accurate delivery and dispatch information to streamline operations, allocate resources effectively, and gain complete visibility. 

16. Safety Monitoring: The constant hum of activity in warehouses and loading docks creates an environment filled with potential accidents including slips, trips, and falls. But with AI video analytics, warehouse managers can monitor hazardous areas, ensure PPE compliance, and receive instant alerts about potential safety issues. 

17. Heatmapping and Traffic Analytics: Massive warehouses are filled with hidden inefficiencies like congested areas, materials in various stages of transport (on pallets, forklifts, or shelves), and often floor layouts that are ill-suited for the dynamic flow of goods. With AI video analytics, enterprises can track movements with heatmaps and real-time foot traffic analytics to identify bottlenecks and optimize layouts to maximize productivity.

Smart Retail

By leveraging the power of video analytics, retailers can create a more efficient, secure, and personalized shopping experience, driving success in the competitive retail industry.

18. Occupancy Counting: Vision AI delivers accurate, real-time data on customer traffic – a critical metric for understanding sales and opportunities to increase revenue. Manual counting is tedious, prone to errors, and pulls staff from more important duties – like driving more sales. Automating this process allows retailers to compare occupancy to sales at various days and times to better capitalize on peak times with enticing incentives.

19. Staff optimization: Labor shortages threaten to cripple retail, leaving shelves bare and frustrated customers waiting in long lines. Strategic staffing is no longer a luxury, it's a survival tactic. Real-time video analytics can help. Track customer flows in checkout lines, by displays, and fitting rooms. Deploy staff strategically, ensuring a smooth shopping experience that keeps customers coming back for more. 

20. Customer Journey Analytics: Knowing which products catch customers’ attention, and where they linger (as well as where they don’t) is valuable information most retailers just don’t have. With advanced AI video analytics, managers can track foot traffic, discover hot spots, and monitor product engagement to optimize product placement and ensure staff is available where customers need them most. 

21. Theft Detection: Over $100 billion vanished from US retailers in 2022 alone, and that number keeps climbing. Constantly monitoring every corner of stores for suspicious activity feels like an impossible task. Not with computer vision. Detect suspicious activity the moment it occurs, sending immediate alerts to team members to safeguard profits, and deter criminals. 

Smart Restaurants

AI video analytics can provide restaurants with invaluable real-time data into their backend and front-of-counter operations to improve processes and ultimately speed up ordering. This is particularly useful for quick-service and fast-casual restaurants looking for greater visibility into food prep and order fulfillment.

22. Behind-the-Counter Operations: Blind spots in the fulfillment process lead QSRs and FCRs to waste food, frustrate customers, and potentially lose money. But with the real-time visual data restaurant managers can see choke points in the food line, identify areas causing delays, and optimize the flow of orders. Additionally, these unprecedented insights empower restaurants to make smarter staffing and inventory decisions, reduce wait times, boost accuracy, and improve customer satisfaction (and profits).

23. Improve speed of service: QSRs and FCRs thrive on speed, but online orders for off-premise dining add a new layer of complexity. Computer vision solutions can track and monitor orders from the moment they're placed, all the way to the customer's hands. Minimize delays, ensure smooth order flow, and keep off-premise service blazing fast with complete behind-the-counter visibility.

24. Drive-Thru Analytics: Seventy percent of QSR and FCR business depends on a smooth drive-thru experience. Speed, accuracy, and safety are the cornerstones of success, but how can restaurants stand out from the competition in a crowded lane? Use Vision AI to count cars in drive-thrus, optimize traffic flow, and ensure orders are fulfilled quickly. Get instant alerts when lines get too long, to deploy staff strategically and keep customers happy. 

25. Occupancy Counting: Relying on hunches for customer numbers leaves money on the table. Historical data is only a hint, and understaffing peak times frustrates customers while overstaffing wastes resources. Real-time AI video analytics reveal exactly how many people are waiting, and for how long. Optimize staffing for peak hours, ensuring a smooth experience that keeps customers satisfied and coming back for more.

The Power of AI Video Analytics in 2024

AI video analytics transcends traditional video surveillance, offering a new dimension of insights and operational efficiencies. At its core, alwaysAI leverages AI to enhance security measures, streamline operations, and personalize customer experiences.

How does my company implement AI Video Analytics in 2024?

Organizations adopting AI video analytics should consider several factors before jumping in. The first is to decide if this kind of project is one they want to take on in-house or if they would prefer to outsource. 

A computer vision platform provides the tools necessary to build applications that deliver AI video analytics but they can vary in terms of customizations and flexibility, level of support, and features. 

At a minimum, an AI video analytics platform should offer the following: 

• Data Collection and Annotation: Effective video analytics starts with collecting and annotating data. This is arguably the most critical step in the process. Without good data, every subsequent step could be compromised. 

Companies first need to identify the specific task or problem they want the video analytics to solve. This helps determine what kind of data to collect from their video cameras. Relevant video footage is then collected and labeled with annotations marking objects, people, or activities of interest in each video frame.

• Model training: Models are like the engine of AI video analytics applications. The annotated video data is used to train a machine-learning model. During training, the model learns to identify patterns and relationships within the video data, allowing it to detect and classify objects, people, or activities of interest in future videos.
• Application development: Application development builds upon the trained model, acting as the bridge between the model's capabilities and real-world use. It involves integrating the trained model into a software application. Developers use specialized tools and libraries to make the model interact with user interfaces, process new data, and potentially connect with other systems. 
• Deployment: Deployment is how you get the applications out into the real world to start analyzing live video streams (or recorded footage). When the application detects an event of interest (intrusion, safety hazard, etc.), it can trigger pre-programmed actions like sending alerts, activating security measures, or integrating data with other systems for further analysis.
• Analytics: The alwaysAI analytics dashboard is where your real-time data gets packaged into actionable insights. It can be customized according to your specific needs, showing you the data most important to your operations. You can use the analytics dashboard to glean patterns and quickly spot aberrations to make better business decisions. What’s more, you can view trends over time to make predictions and get a comprehensive view of your operations.  

There is quite a bit of variability in terms of the features and capabilities each platform has. Before implementing an AI video analytics system, evaluate your goals and current infrastructure like hardware and security. Depending on the deployment method (cloud or edge), companies may need to invest in additional hardware like high-resolution cameras and/or edge computing devices. Companies need to ensure they comply with data privacy regulations when collecting, storing, and using video data for AI analytics.

How to Choose the Right AI Video Analytics Software?

The precise computer vision tools and features of each platform can vary widely. To select the appropriate AI video analytics software, first, identify your goals. What actions or insights do you hope to achieve? This will determine the functionalities most important to you, such as object detection, anomaly recognition, or people counting. 

Enterprises should also consider factors like real-time processing capabilities. For some, the ability to capture and process real-time visual data is paramount. For example, in a large manufacturing facility where every minute of machine downtime costs money, real-time data is essential to maximizing productivity. For others, like a restaurant looking to understand parking lot utilization, the video analysis may need to be analyzed across an entire day but not necessarily in real-time. 

Lastly, enterprises need to assess their technical expertise and infrastructure. Consider if you need user-friendly interfaces for ease of use, or conversely, a platform offering extensive customization. Ensure compatibility with your existing camera systems and data storage solutions. Finally, explore pricing models and ongoing support options to ensure they align with your budget and technical comfort level.

Why Choose alwaysAI as Your AI Video Analytics Provider? 

alwaysAI is a full-service, real-time, AI video analytics provider. We work closely with clients to understand their long and short-term goals and budget, and then provide recommendations on the most applicable use cases be it object detection, event recognition, defect detection, or something else. Together we assess the level of support you need and want to set expectations. 

alwaysAI's platform is designed for easy deployment and integration, catering to a wide array of practical AI solutions and accessible to any enterprise. This includes those with internal Python developers just looking to leverage our platform as well as those looking to outsource their entire computer vision projects (and everyone in between!).  

While many computer vision platforms claim comprehensiveness, alwaysAI goes a step further. We empower you to not only build powerful solutions but also leverage our expert team to ensure success. Here's how alwaysAI stands apart from other AI video analytics providers:

Quick, Convenient Data Management: Capture, organize, and annotate new or existing data in one centralized tool.

• Upload images, videos, or complete datasets for maximum flexibility. Share and collaborate on datasets across teams for efficient data preparation.
• Leverage auto-annotations, using semi-supervised learning to create complete datasets in record time. For further convenience use tracking, hotkeys, and interpolation. 
• Advanced video sampling allows users to preview and configure sample rate to ensure optimal data distribution.

Streamlined Model Training: alwaysAI is a leader in model training thanks to unparalleled flexibility.

• Choose from our ever-expanding library of pre-built model architectures, customize parameters and hyperparameters including image size, batch size, epochs, GPUs, and early stopping.
• Or bring your own model (or your own model architecture).
• Get estimated training times and track session performance in real-time.
• modelIQ, our unique evaluation tool, pinpoints detection accuracy by label, size, and image quadrant so you can refine model training for faster deployment.

Rapid Application Development: Leverage alwaysAI’s extensive edgeIQ library of APIs to create computer vision applications that deliver impactful results. 

• Our team actively uses and refines our API library (edgeIQ), simplifying complex tasks and accelerating development.
• Leverage Python 3.7-3.11 for flexibility and utilize edgeIQ's Multi-Stream Framework to build and manage efficient multi-camera applications effortlessly.
• Jumpstart development with our extensive library of pre-built starter applications.

Scalable Remote Deployment: Easily deploy, manage, and scale computer vision models and applications on-site or remotely. 

• Deploy applications to the edge, cloud, or on-premise servers with ease. Scale quickly and manage everything remotely.
• Enjoy robust security features, including data encryption in transit and secure remote reboot, eliminating whitelisting and custom networking hassles.
• Push to multiple devices and manage and monitor device details (CPU, storage, network) and applications remotely with a single command.

Actionable Insights at Your Fingertips:

• Our dashboard tailors real-time data and analytics to your specific needs, whether it's customer wait times, machine utilization, or PPE compliance.
• Prefer to stick with analytics or BI tools you’re already using? No problem. alwaysAI can integrate real-time data with your existing analytics tools for a unified view.

Unparalleled Support:

• Future-proof your AI Video Analytics solutions with all the tools and features necessary to adapt and scale as your business evolves.

Our world-class Vision AI team provides comprehensive support, from initial concept to model and application development to ongoing maintenance.

Future Trends in AI Video Analytics

As technology evolves, so too does the landscape of AI video analytics. Future trends indicate a closer integration with IoT devices and an instrumental role in the development of smart factories, smart industries, smart retail stores, and more.

AI Video Analytics in the Era of IoT

The convergence of AI video analytics and IoT devices is set to offer unprecedented levels of operational intelligence, enabling real-time responses to a wide range of scenarios, from traffic management to environmental monitoring. Cameras that seamlessly integrate with a network of sensors, to deliver real-time data could be a game-changer for nearly any industry.

For instance, a smart factory might utilize facial recognition to identify unauthorized personnel and trigger security measures. Meanwhile, thermal cameras coupled with AI could detect equipment overheating, prompting preventive maintenance alerts before breakdowns occur. The possibilities are endless, with applications in traffic management, retail analytics, and even personalized healthcare. This interconnected future promises enhanced security, optimized operations, and a new level of proactive decision-making.

Because cameras are already widely used across enterprises, upgrading to AI-powered video analytics will quickly become the norm – especially as AI technology continues to improve and become more ubiquitous. 

The Role of AI Video Analytics in Smart Cities

In the context of smart cities, AI video analytics will play a pivotal role in enhancing public safety, optimizing traffic flow, and improving urban planning, contributing to more livable, efficient, and sustainable urban environments.

AI video analytics is poised to become the cornerstone of future smart cities with traffic lights that dynamically adjust based on real-time congestion, pedestrian crossings that flow safely and smoothly and public transportation routes that are efficient. Public safety will be enhanced with AI identifying suspicious activity and enabling faster emergency response times. Moreover, video analytics will contribute to environmental sustainability by monitoring waste disposal and optimizing energy use in public buildings. In essence, AI video analytics will act as the intelligent eyes of the city, gathering real-time data to optimize traffic flow, enhance public safety, and create a more efficient and sustainable urban environment.

FAQs

Q: What industries can benefit from AI video analytics?

A: A wide range of industries including manufacturing, mining, energy, logistics, retail, and more can harness the benefits of AI video analytics for enhanced operational efficiency and customer experience. AI video analytics is a powerful solution that can help any business with repeatable processes. If you can envision a use case where an extra set of eyes watching a particular area or process would be useful, AI video analytics can help. 

Q: How does alwaysAI simplify the implementation of AI video analytics?

A: alwaysAI simplifies the process through a comprehensive platform that includes dataset management, model training, starter apps, advanced and comprehensive APIs, remote deployment, and analytics, allowing businesses to focus on the insights delivered by AI. With one truly end-to-end platform, users can seamlessly create powerful applications from start to finish or jump into the platform at any point without having to retrain completed models, datasets, or applications. 

Building AI video analytics without a platform is possible but complex and time-consuming. It would require using several separate tools for annotations, model training, application development, and deployment. You’d run the risk of one tool not integrating with another. You’d lack a resource to turn to for questions and support. Making changes to models or applications could result in starting over.  

alwaysAI has a world-class vision team that doesn’t just build and simplify the AI video analytics platform, they use it every day. They understand what tools and features help streamline processes and are continually working to simplify the process so you can get the real-time visual data you need to be successful. 

Q: Can AI video analytics be integrated into existing video surveillance systems?

A: Yes, alwaysAI's platform is designed for seamless integration with existing video surveillance systems, enhancing their capabilities with advanced AI analytics. Our platform is hardware and camera agnostic, meaning we can work with nearly any kind of camera (i.e. fisheye lens, drones, CCTV, and other specialty cameras) and can sometimes be trained using existing video footage. 

Our Vision AI team will assess factors like lightning, camera angle, obstructions, or issues with your existing video surveillance that could impact AI video analytics applications. We can then help you evaluate what, if any, changes need to be made to existing video surveillance systems. 

Q: What are the key considerations when choosing an AI video analytics solution?

A: Key considerations include the solution's ability to process data in real-time, scalability, ease of integration, and the level of support provided.

Real-time detections: For many enterprises, the most valuable aspect of AI video analytics is the ability to capture visual data in real-time. Nearly every business, across industries, can benefit from an extra set of eyes that are always watching, never tire, and can process information in real-time. 

Scalability: As your business grows, you need AI video analytics solutions that can grow with you. Whether you expand to multiple locations, expand your current operations, or discover new use cases for this incredible technology, alwaysAI makes scaling applications easy at every step of the process. Each of our dataset management features like semi-supervised learning, advanced video sampling, and functional annotation tools are designed to make building robust datasets quick and easy. 

Our model training allows you to leverage our menu of pre-trained models or bring your own model or model architecture. You can choose from an advanced selection of model parameters and hyperparameters including image size, epochs, multiple GPUs, and early stopping as well as get forecasted model training results, real-time training session performance and critical insights into model performance with our model evaluation tool, edgeIQ. Each of these is intended to help you train and scale models more easily. In terms of application development, alwaysAI’s unparalleled APIs make it quick to build and iterate applications for seamless scaling.

Lastly, our remote deployment is unbeatable in deploying, managing, collaborating, and pushing applications to multiple devices for scaling that’s just not possible with other platforms. We provide ALL the tools and features you need to scale quickly without compromising quality.