The Business Edge of AI Vision: Turning Insights into Action
AI Vision in Motion – Turning Machines into Context-Aware Storytellers
Imagine AI not just as an observer, but as a storyteller—one that connects each frame in a video sequence to understand and predict events, much like humans. This is the essence of motion-based computer vision, where AI goes beyond static images to interpret actions and anticipate outcomes in dynamic environments.
This second installment in computer vision expands from analyzing still images to interpreting motion, highlighting how industries are leveraging this technology.
The blog concludes with a noteworthy mention of embedUR and ModelNova. Focusing on how the platform facilitates seamless AI integration on low-power devices, enabling real-time video analysis even with minimal resources, enabling developers with little to no AI experience to experiment with models and build Edge AI proof of concept demos in days or weeks, not months.
From Still Frames to Mastery of Motion: The Rise of Computer Vision in Video Analysis
The journey of computer vision began with the analysis of individual still images, where AI algorithms were designed to detect objects or classify images based solely on static data.
This foundational approach was essential, but limited, as it lacked the “temporal depth” (refers to the richness or completeness of information derived from observing transitions between frames over time) present in video sequences.
As machine learning advanced, researchers recognized the value of video analysis, which contains a sequence of frames that provide richer contextual information.
This shift, driven by innovations like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), allowed AI to capture both spatial patterns and temporal changes, essential for understanding motion and predicting actions.
Unlike static images, video provides context, enabling AI to understand movement and continuity. This capability is crucial in fields like autonomous driving, where recognizing interactions between pedestrians, vehicles, and road signs informs real-time decisions.
Video-based AI enhances capabilities in security, where it monitors live feeds to detect suspicious behavior—decades ago, video surveillance was a painstaking manual process,as security guards would sit for hours, monitoring countless screens, hoping to catch suspicious activities amidst a sea of normalcy. Today, AI and CV (Behavioral recognition technology) has led to the automation of anomaly detection and analyses of real-time video streams with precision.
The integration of deep learning models like AlexNet, ResNet, and LSTM has further advanced video classification, enabling applications from real-time surveillance to dynamic user experiences in interactive media. These advancements position computer vision as a critical tool for building efficient, adaptable systems across industries.
Five High-Impact Use Cases of Computer Vision in Moving Images
Computer vision has become a game-changing force across industries, particularly through its applications in moving images. Leveraging advanced algorithms and AI, these applications not only enhance efficiency but also provide real-time insights that improve accuracy, safety, and user experiences across sectors.
It is already in use more than you might imagine (while some of the technologies used rely on Edge base AI, most are often supplemented by cloud-based AI for broader data aggregation and analysis).
Here are five of the most impactful ways computer vision is used to interpret moving images:
1. Object Tracking
Object tracking is a vital computer vision technique that enables machines to follow the movement and predict the location of objects across frames in video sequences. This process is widely applied across industries, enhancing everything from surveillance systems to automated quality checks.
Object tracking algorithms generally follow four essential steps: preprocessing the input (using OpenCV), detecting and classifying objects (with object detection algorithm), labeling each detected object with a unique identifier, and maintaining the trajectory of each object across frames.
Object Tracking in Sports
Object tracking is revolutionizing sports analytics by providing detailed insights into player and ball movements. For example, IBM’s cognitive coaching system for the U.S. Women’s National Soccer Team analyzes player performance in real-time, helping coaches adjust strategies based on data-driven insights.
Sports like cricket and baseball use object tracking to enhance gameplay strategies by tracking ball trajectories and helping batters adjust their swing for better accuracy. This has elevated sports by enabling deeper performance analysis for players and teams alike.
By tracking the movement of athletes, coaches can identify areas to make improvements, and athletes can see where they need to focus their training. Recent advancements also enable computer vision to track athlete joint and limb movements, refining performance metrics and injury prevention.
This data can be used to improve team performance by helping coaches and athletes to understand how they are responding to training, and what areas need improvement. In addition, this data can be used to prevent injuries by identifying the potential risks.
2. Scene Understanding
Scene understanding is the process of perceiving and interpreting a complex environment in real time, often involving 3D perception through sensor networks. This capability is especially critical in autonomous driving, where accurate interpretation of the surroundings is crucial for safe navigation.
Scene understanding combines computer vision, cognitive processing, and software engineering to achieve functions like object detection, localization, recognition, and an overarching interpretation of the scene.
Scene Understanding in the Automotive Industry
Autonomous driving heavily relies on scene understanding to navigate safely. Companies like Waymo and Lyft deploy this technology in their self-driving cars, using AI to identify obstacles, road layouts, traffic signals, and other vehicles.
While the computer vision technology used by self-driving car companies like Waymo, Tesla, and Lyft primarily relies on Edge AI for real-time operations, the combination of Edge and Cloud AI ensures both immediate responsiveness and long-term system improvement.
This technology has contributed to a reported 85% reduction in injury-related accidents in Waymo’s autonomous vehicles compared to human driven cars. As ride-hailing services increase, scene understanding allows autonomous vehicles to navigate cityscapes and offer safer rides.
This unwavering trust in Waymo’s safety-first technology has fueled remarkable growth, with the company’s weekly rides soaring from 10,000 to 100,000 within just a year—an achievement that underscores the growing public confidence in autonomous mobility.
3. Human/Object Pose Estimation
Pose estimation focuses on detecting and tracking semantic key points, such as shoulders, knees, or vehicle lights, to understand posture and movement. For human applications, this technique is widely used in sports and fitness to analyze form, while for objects, it enables accurate positioning in industries like robotics and automotive.
Pose Estimation in Virtual and Augmented Reality
Pose estimation has become a core element of virtual and augmented reality (VR/AR) applications. In AR, for example, The United States Army is experimenting with pose estimation for combat simulations, helping soldiers identify enemy versus friendly forces and improving situational awareness.
Through the combination of Cloud base and Edge AI, Apple Vision Pro also utilizes pose estimation in AR applications, tracking users’ body movements to enable immersive experiences in gaming, fitness, and interactive training. This technology is creating a new frontier in training and simulation by providing real-time feedback based on precise body tracking.
4. Behavioral Recognition
Behavioral recognition identifies and categorizes actions by analyzing sequences of frames to detect specific behaviors or patterns.
A leading application of this technology is Paladin’s AI-powered drones, which support first responders by analyzing real-time situations such as crowd dynamics, traffic incidents, public gatherings, and emergency situations where rapid movement or panic occurs.
Deep learning techniques like LSTM Networks and Hidden Markov Models (HMMs) are used to capture these temporal patterns, making behavioral recognition a powerful tool in surveillance, public safety, and security.
Behavioral Recognition in Video Surveillance
Behavioral recognition in surveillance systems enables proactive safety measures in public spaces like malls, stadiums, airports, and schools. These systems can detect unusual or suspicious behavior (like running, unusual gatherings, loud noises, e.t.c.) and provide real-time alerts, significantly enhancing security.
In the retail sector, behavioral recognition has proven essential—retail giants like Walmart are now using behavioral recognition technology to reduce losses, showing significant improvements in theft detection and prevention.
With the National Retail Federation reporting a rise in theft-related losses to $112 billion in 2023, the integration of intelligent surveillance systems is now a must. AI is revolutionizing how public safety is managed by reducing human error and enabling swift responses to potential security threats.
5. 3D Object Reconstruction
3D object reconstruction is the process of creating a three-dimensional representation from 2D images or other data sources. This technique is used for applications like virtual model generation, heritage preservation, and detailed analysis in various industries. By creating virtual 3D models, it provides a means for visualization, simulation, and further analysis of complex objects or environments.
3D Object Reconstruction: A Tool for Cultural Preservation
Organizations like CyArk use 3D object reconstruction for digital preservation of cultural heritage sites. By combining technologies such as LiDAR scanning and photogrammetry, CyArk has created accurate 3D models of historic locations, preserving both physical details and historical narratives.
These models not only make cultural heritage accessible to a wider audience but also provide valuable data for restoration and preservation efforts, ensuring that future generations can experience these treasures even if the physical sites are damaged or altered over time.
Unlocking Business Potential with AI Vision
“Forget hindsight; with AI vision, your business has foresight—every frame is a leap into actionable insights.”
Integrating AI vision, for moving images, into your business opens unparalleled opportunities for enhanced decision-making, efficiency, and customer engagement. From real-time video analysis to dynamic object tracking, AI-driven video analytics provide deeper insights by capturing motion and context.
For example, Amazon is leveraging AI-powered cameras (for automated video analysis) in its warehouses to streamline operations, reducing error rates by up to 40% and optimizing inventory management, while also ensuring a safer environment by monitoring employee interactions with equipment.
In autonomous vehicles, companies like Tesla and Waymo rely on advanced video analytics to interpret real-world environments, enabling safe navigation by tracking the movement of pedestrians, vehicles, and road signs.
Tesla also reports that after the release of its “Unboxed” manufacturing method (which relies on computer vision for advanced monitoring), the overall size of the factory footprint has been reduced by about 40% and potentially cutting costs by up to 50%.
McKinsey reports that implementing AI-driven solutions like these can increase operational productivity by as much as 25%, translating to substantial gains for businesses in logistics and transportation.
For retailers, computer vision in video analysis enhances customer experiences through interactive displays and personalized shopping suggestions—this involves the use of AI-powered cameras to monitor inventory in real-time.
Leveraging advanced algorithms, the system converts this data into actionable insights, providing personalized recommendations and optimizing the shopping experience for each customer. This has led to an increase in engagement and a boost in sales conversion rates by up to 30% in AI-optimized environments.
As we move further into an AI-enabled future, incorporating video-based AI vision not only makes businesses more efficient, it also keeps them competitive in a rapidly evolving landscape.
Embracing the Future with Computer Vision
In 2023, the global computer vision market was valued at USD 20.31 billion and is projected to reach USD 175.72 billion by 2032, growing at an impressive compound annual growth rate (CAGR) of 27.3%. North America dominated market share with 30.97% in 2023.
A key driver of this growth is the increasing adoption of AI-powered vision systems across various sectors, including agriculture, where they are reshaping processes such as crop monitoring and enhancing productivity.
While Advertising and Media, BFSI (Banking, Financial Services, and Insurance), and Healthcare currently account for the top 3 largest revenue shares in the global AI market, the healthcare sector is projected to take the lead by 2030.
The reason being that the sector spans use cases such as robot-assisted surgery, dosage error reduction, virtual nursing assistants, clinical trial participant identification, hospital workflow management, preliminary diagnosis, and automated image analysis.
The true potential of AI lies not just in its capabilities but in its ability to be seamlessly integrated into everyday tools and devices. At embedUR, we’ve mastered the art of computer vision, enabling machines to see, interpret, and respond like never before. Now, the challenge—and opportunity—lies in scaling this power down to small, energy-efficient devices. This is where ModelNova takes the centre stage.
You can leverage embedUR’s unique expertise in IoT and AI and Edge AI resources available in our Edge AI development hub, to turn your AI Vision ideas into real applications.
ModelNova’s pre-trained AI models, designed for easy deployment on low-power devices, allow businesses to quickly implement AI solutions and bring new products to market faster.
Computer vision has existed in various forms for a long time, but we are only beginning to see its full potential. With AI now capable of running on small, low-powered devices, there will be an explosion of AI-driven computer vision applications.
This growth will be fueled by resources like ModelNova, a valuable tool for Edge AI engineers with a repertoire that enables rapid development of proof of concepts and entirely new applications.
Still on the sidelines? Contact us today to bring your vision to life.
