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Unlocking the Power of Large Visual Models in Video Analytics: Opportunities, Challenges, and the Critical Role of On-Premise Deployment


The advent of Large Visual Models (LVMs) is revolutionising the field of video analytics. These advanced AI systems, akin to Large Language Models in text processing, are designed to understand, interpret, and make decisions based on vast volumes of visual data. In this article, we’ll delve into what LVMs are, their transformative impact on video analytics, the challenges of implementation, and why on-premise deployment is vital for critical infrastructure.

What Are Large Visual Models?

Large Visual Models (LVMs) are AI models trained on massive datasets of images and video. They leverage state-of-the-art deep learning architectures, such as convolutional neural networks (CNNs) and transformers, to recognise patterns, detect anomalies, and interpret complex visual data with remarkable accuracy.

LVMs enable video analytics solutions to:

  • Detect unusual behaviours or objects in real time.
  • Classify activities with high precision.
  • Provide contextual understanding for decision-making.

For industries like security, transportation, and critical infrastructure, these capabilities are transformative, offering unprecedented levels of automation and insight.

Impact on Video Analytics

The integration of LVMs into video analytics has brought about significant advancements:

  • Enhanced Accuracy

LVMs can process vast amounts of visual data, reducing false positives and improving anomaly detection.

  • Scalability

With their ability to analyse multiple video streams simultaneously, LVMs are ideal for large-scale deployments.

  • Proactive Security

These models enable predictive capabilities, allowing systems to anticipate potential threats before they escalate.

  • Automation

LVMs reduce reliance on human operators for routine monitoring, enabling teams to focus on critical tasks.

Challenges in Implementation

While the potential of LVMs is undeniable, their implementation is not without hurdles:

  • Data Requirements: Training and fine-tuning LVMs require vast, diverse datasets. Acquiring and curating such data can be a significant challenge.
  • Computational Demands: LVMs are resource-intensive, requiring substantial processing power for both training and inference.
  • Privacy Concerns: Handling sensitive video data, especially in industries like healthcare and critical infrastructure, raises privacy and compliance issues.
  • Complexity of Deployment: Integrating LVMs into existing systems demands specialised expertise and careful planning.

The Case for On-Premise Deployment

For critical infrastructure, on-premise deployment of video analytics systems powered by LVMs is not just preferable—it’s essential. Here’s why:

  • Data Privacy and Security: Sensitive data never leaves the local environment, reducing the risk of breaches and ensuring compliance with regulations.
  • Low Latency: On-premise systems eliminate the delays associated with cloud communication, enabling real-time decision-making crucial for security.
  • Operational Continuity: Critical infrastructure must function even during internet outages. On-premise systems ensure uninterrupted operations.
  • Customisability: On-premise solutions can be tailored to specific use cases and integrated with existing infrastructure seamlessly.

Why Avoid Agents Talking to the Cloud?

Deploying video analytics solutions that rely on cloud communication poses significant risks and limitations for critical environments:

  • Latency Issues: Cloud-based systems depend on stable, high-speed internet connections. In critical scenarios, delays can have severe consequences.
  • Data Vulnerability: Transmitting sensitive video data to the cloud exposes it to potential cyber threats.
  • Compliance Challenges: Many industries have strict regulations prohibiting the transfer of sensitive data off-site.

By deploying LVMs on-premise, organisations maintain control over their data while ensuring the highest levels of security and operational reliability.

The Future of LVMs in Video Analytics

The integration of LVMs into video analytics is just the beginning. As these models become more efficient and accessible, we’ll see even greater adoption across industries. However, to unlock their full potential, organisations must navigate the challenges of implementation with a focus on data security and operational efficiency.

At IntelexVision, we’re proud to lead this charge with iSentry, powered by Aurora — a pioneering first-generation AI solution for security. By prioritising on-premise deployment and tailored solutions, we’re not just imagining the future of video analytics but building it today.

Let’s Connect!

Interested in learning more about how LVMs can transform your video analytics? Let’s discuss how IntelexVision can help your organisation leverage cutting-edge AI technology for a safer, smarter future.

Author: rdadminzik

Date: 2025-01-31

Intelex Vision raises £5.6m ($ 7.0m) Series A Funding


London, 13 January 2025. Intelex Vision, the pioneering company specialising in AI-driven video analytics for real-time surveillance, is pleased to announce it has successfully closed a £5.6m ($7.0m) Series A funding round, backed by Acurio Ventures, Adara Ventures and Inveready, among others.

Callum Wilson, co-CEO of Intelex Vision commented: “this strategic investment allows us to fuel our commercial growth whilst continuing to invest heavily in the product and disruptive and differentiated AI technology that underpins it”.
Michael Vorstman, co-CEO added “after growing our revenues 11x over the past 3 years, in this new phase of our development we will aim to not only strengthen and expand our position in our existing markets, but also start to address some of the largest ones globally”.
Hugo Fernández-Mardomingo, partner at Acurio Ventures said: “Given the rising insecurity perception in cities and critical infrastructure projects, we see Intelex Vision technology as a game changing opportunity to shift the status quo from forensic post-event data analysis to realtime threat monitoring. We are delighted to be part of this new growth phase in the video analytics industry, and to partner with the outstanding and dynamic team behind Intelex Vision.”
Nico Goulet, Founding Partner at Adara Ventures added: “As large-scale CCTV deployments generate ever-growing amounts of data, smarter video analytics are key for real-time threat detection and decision-making. With a scalable solution proven in the most challenging and dynamic environments, Intelex Vision has emerged as a leader in AI-powered video analytics, and we are proud to continue supporting their growth.”
Ignacio Fonts, non-executive Chairman of Inveready: “We are happy to see that the excellent performance of Intelex in the recent years has been rewarded with the closing of this important fundraising milestone. We are confident that the combination of a great team, a unique technology and the market potential of AI-powered video analytics will turn Intelex into a shining star among the scaleups of Europe.”

-END-

About Intelex Vision
Founded in 2017, Intelex Vision is a leading global provider of advanced AI-powered surveillance solutions that leverages 4th generation artificial intelligence to autonomously monitor, analyse and detect safety & security threats in real-time. With a global presence across five continents and partnerships with over 70 distributors and technology partners, Intelex Vision’s AI processes over 2 billion hours of video every month, transforming sectors that include critical infrastructure, transportation, healthcare and urban environments enabling faster response times and improved operational efficiency.
www.intelexvision.com

About Acurio Ventures
Acurio Ventures partners with visionary founders to make history happen. Acurio Ventures is a European early-stage VC investing across sectors and business models, and that relies on its entrepreneurial DNA, flexible approach and proven value add to partner with the best founders. Almost seven years after Acurio Ventures kicked off operations, and after having invested in 90+ companies and more than a dozen funds, Acurio Ventures manages almost €300m across four investment vehicles.
www.acurio.vc

About Adara Ventures
Adara Ventures invests in ambitious businesses, partnering with founders with the capacity, courage, and vision to execute. In the last 20 years, the firm has invested in over 47 companies and manages €250m. Focused on early-stage companies and with a European footprint, Adara’s portfolio includes companies dedicated to cybersecurity, data applications and infrastructure, DevOps, hardware components, digital health, and the energy transition.
www.adara.vc

About Inveready
Inveready is a leading alternative asset manager in Southern Europe with €1.6bn+ of assets under management and a strong focus on high-growth technology companies. With more than 15 years of track record and 64 exits materialised, it provides financing solutions to companies throughout their life-cycle from startup phase up to mature stages.
www.inveready.com

Author: rdadminzik

Date: 2025-01-13

IntelexVision to obtain NCAGE code



IntelexVision proudly announces that the United Kingdom National Codification Bureau has allocated the UK NCAGE code U2B97 to us.

The NCAGE (NATO Commercial and Government Entity) code is a unique identifier that opens up a world of opportunities in the international arena related to NATO operations. With its help, we can enter global markets, participate in NATO tenders, and secure large contracts with government and commercial organisations.

Author: rdadminzik

Date: 2024-12-06

The Inference Barrier in Artificial Intelligence: Challenges and Impacts on AI Video Analytics


Introduction

The exponential growth in artificial intelligence (AI) applications across various industries has notably enhanced operational efficiencies and analytical capabilities. Among these applications, AI video analytics has emerged as a transformative technology, particularly in sectors such as security, transportation, and retail. However, as with any rapidly evolving technology, challenges are plentiful. One significant challenge is the “inference barrier” – a term that encapsulates the difficulties AI systems face when interpreting and predicting based on complex data inputs and analysing new examples. This article explores the inference barrier within the context of AI video analytics, detailing its challenges and impacts on the field.

Understanding the Inference Barrier

The inference barrier in AI refers to the limitations and challenges associated with an AI model’s ability to process, interpret, and infer information from data inputs effectively. In the world of AI video analytics, this barrier is often encountered due to the complexity and volume of data, as well as the subtleties involved in interpreting visual cues in real-time.

Key Challenges Posed by the Inference Barrier

High Dimensionality of Data: Video data is inherently high-dimensional, making it challenging for AI models to process efficiently. Each frame of a video can contain vast amounts of information, and when multiplied by the number of frames per second, the data becomes colossal.

Real-Time Processing Needs: AI video analytics often require real-time processing and inference to be effective, particularly in applications like surveillance or traffic management. The inference barrier becomes apparent when latency issues arise, hindering the ability to make timely decisions.

Accuracy and Reliability: The subtleties of human behaviour and the variability in environmental conditions (e.g., lighting, weather) can affect the accuracy of AI inferences. Misinterpretations and errors in object recognition can lead to significant repercussions, especially in critical applications.

Training Data Biases: AI models are only as good as the data on which they are trained. Biased or insufficient training data can exacerbate the inference barrier, leading to flawed or skewed AI interpretations. That’s why self-learning algorithms display the advantage here.

Impact on AI Video Analytics

The inference barrier significantly impacts the efficiency and effectiveness of AI video analytics in several ways:

Security Applications: In security, the need for precise and accurate threat detection is paramount. An inference barrier can lead to false positives and negatives, potentially causing either unwarranted alarms or overlooked security threats.

Transportation Systems: AI-driven traffic monitoring and management systems rely on real-time data interpretation to adjust signals and manage flows. Delays or inaccuracies in inference can lead to traffic congestions and accidents.

Retail and Customer Behaviour Analysis: In retail, video analytics are used for customer behaviour tracking and store management. An inference barrier might misinterpret customer actions, leading to incorrect business decisions and strategies.

Example in Security Systems

Consider a scenario where an AI-equipped security system utilises video analytics to monitor a busy public space for potential threats. If the inference barrier plays a role, the system might misinterpret a non-threatening activity as a security threat due to poor lighting conditions or obscured visuals. This could trigger an unnecessary lockdown or evacuation, causing panic, disrupting normal activities, and wasting emergency response resources. Conversely, the same barrier could fail to identify a genuine threat, resulting in a security breach.

Overcoming the Inference Barrier

Advancements in computational power, algorithms, and data collection methods are continually being developed to overcome the inference barrier. The integration of edge computing with AI video analytics helps reduce latency by processing data closer to the source. Improved training techniques, such as synthetic data generation and advanced neural network architectures, also enhance the AI’s ability to learn from complex video inputs more effectively.

Furthermore, continuous monitoring and updating of AI systems are crucial to adapt to new data and changing conditions, ensuring models remain relevant and accurate over time.


However, there is an existing system called iSentry from IntelexVision that overcomes the inference barrier. It utilises an innovative technique to split detection and classification into layers. The first layer is a self-learning AI, which analyses the patterns of the camera scene and learns from this specific field of view how objects behave and interact in unbiased way. It creates a normality pattern at the pixel level and extracts any anomalies for the next layers. This process reduces the amount of video data to be analysed by 95%, significantly impacting the ability to overcome the inference barrier. Subsequent layers could incorporate various deep learning neural networks, such as those for detecting fighting, guns, falls, or running. Additionally, the Generative AI, Aurora, can contextualise the alert to provide the operator with full context.
An additional rule engine allows the user to display only the critical alerts relevant to them and provides any necessary security procedures.

This system synthesises the video timeline, delivering only relevant alerts to the operator in real time.

Conclusion

The inference barrier presents a significant challenge to maximising the potential of AI video analytics. By addressing these challenges through technological advancements and better data management practices, the efficacy of AI systems can be markedly improved. As AI continues to evolve, the focus on overcoming the inference barrier will play a critical role in enabling #AI video analytics to achieve its full potential in various industries. Choose wisely, choose iSentry.

Author: rdadminzik

Date: 2024-10-28

Moravec’s Paradox and the Challenges of AI in Video Analytics


In the era of Artificial Intelligence (AI), industries are increasingly adopting AI technologies to improve processes, automate workflows, and enhance decision-making. One promising application is in video analytics, where AI systems can analyse visual data for security, marketing, healthcare, and many other sectors. However, the development of effective AI video analytics is not without its challenges—many of which are encapsulated in Moravec’s Paradox.

Understanding Moravec’s Paradox

Moravec’s Paradox, named after AI researcher Hans Moravec, highlights a curious phenomenon: tasks that are cognitively complex for humans—such as solving math problems or playing chess—are relatively simple for AI, but tasks that seem easy for humans, like recognising faces or moving through an environment, are extremely difficult for machines. This is especially relevant in the world of video analytics, where machines are expected to interpret complex visual data in real time.

The paradox arises because human brains have evolved over millions of years to perform sensory and motor tasks—such as walking, recognising objects, or making quick decisions in dynamic environments—effortlessly. By contrast, high-level reasoning and logical problem-solving are relatively recent evolutionary developments, requiring less processing power compared to sensorimotor tasks.

The Role of AI in Video Analytics

AI video analytics has tremendous potential across industries, from enhancing security systems with facial recognition and movement detection to helping retailers analyse customer behaviour in-store. The ultimate goal of AI in video analytics is to automate the recognition and interpretation of visual patterns to provide insights or trigger actions without human intervention.

For instance, an AI video analytics system in a retail store could analyse foot traffic, identify peak hours, and even suggest optimal product placement based on customer behaviour. In healthcare, AI can monitor patients in real time, detecting unusual behaviour such as falls or abnormal movements.

The Challenge of Moravec’s Paradox in AI Video Analytics

Despite its promise, video analytics is particularly vulnerable to Moravec’s Paradox. While AI can process enormous amounts of data and detect certain patterns with precision, it often struggles with tasks that humans find effortless, such as distinguishing between a human figure and a shadow, or recognising the nuances of facial expressions.

Some key challenges that arise from Moravec’s Paradox in AI video analytics include:

  1. Object Detection and Recognition: AI systems may struggle with identifying objects in cluttered, poorly lit, or visually complex environments. Where a human would immediately identify a person walking through a crowded street, an AI system could struggle with overlapping objects, varying lighting conditions, or occlusions.
  2. Contextual Understanding: Humans can easily understand context in visual scenarios, such as distinguishing between a person running in a park for exercise and a person running away in panic. AI video systems, however, may find it difficult to interpret these contextual differences without vast amounts of training data.
  3. Real-Time Processing: Real-time video analytics requires the AI to process and respond to visual data quickly and accurately, something humans do instinctively. While AI systems can process frames rapidly, the ability to make context-sensitive decisions on the fly (e.g., recognising an unusual movement in a security feed) remains a significant challenge.
  4. Facial Recognition and Emotion Detection: AI video analytics systems can identify faces with increasing accuracy, but nuances like emotions, subtle facial expressions, and changes in mood are still beyond many systems’ capabilities. Humans effortlessly recognise when someone is happy, sad, or confused, but AI needs extensive training and still struggles in diverse, real-world settings.

Overcoming the Paradox: How IntelexVision Addresses the Challenges

Despite the inherent difficulties outlined in Moravec’s Paradox, IntelexVision made remarkable progress in overcoming these challenges, particularly within the field of AI video analytics. By leveraging advanced technologies such as self-learning neural networks, transfer learning, and innovative approaches to contextual interpretation, IntelexVision is pushing the boundaries of what AI can achieve in visual data analysis.

  1. Self-Learning Neural Networks: IntelexVision uses sophisticated, self-learning neural networks that are capable of improving their performance over time without constant human intervention. These networks can adapt to different environments, recognising and categorising objects more effectively even in complex or crowded scenes. For instance, in a security context, IntelexVision’s systems can differentiate between normal pedestrian movement and suspicious behaviour, learning from real-world data to enhance accuracy and reliability.
  2. Transfer Learning Across Tasks: One of the key ways IntelexVision overcomes Moravec’s Paradox is by utilising transfer learning, where the knowledge gained from one task is applied to new but related tasks. For example, after training the AI to recognise objects in static images, the system can transfer this knowledge to interpreting dynamic video footage. This allows the AI to track objects in real-time, even when they move unpredictably or in low-visibility conditions, enhancing the system’s adaptability to new environments and scenarios.
  3. Contextual Understanding Through Multi-Neural Network Collaboration: IntelexVision employs multiple neural networks that work collaboratively, each focused on different aspects of the video feed. For example, one network may specialise in object detection, while another focuses on analysing movement patterns. This collaborative approach enables the system to better interpret context—such as distinguishing between someone running for exercise and someone fleeing in distress. By integrating insights from multiple networks, the system becomes more adept at making context-aware decisions.
  4. Adaptive Learning for Real-Time Responsiveness: IntelexVision has also developed AI systems that excel at adaptive learning, allowing them to process and react to video data in real time. The system continuously refines its ability to identify key patterns, such as detecting unauthorised access in security settings or spotting anomalies in industrial environments. This responsiveness is critical in real-time video analytics, where immediate action may be necessary based on the system’s observations.
  5. Enhanced Edge Computing Capabilities: To further mitigate the challenges of real-time processing, IntelexVision integrates edge computing into its video analytics solutions. By processing video data closer to its source, the system can reduce latency and ensure faster decision-making, a key factor in applications such as security monitoring. This approach not only increases the speed of data analysis but also helps maintain privacy and security, as less sensitive data needs to be sent to the cloud.

The Road Ahead

AI video analytics is one of the most exciting and rapidly developing fields of AI, but it also highlights the limitations described by Moravec’s Paradox. While machines can be trained to recognise patterns and detect anomalies, replicating the effortless way humans interpret complex visual data in real-time remains a daunting task.

As AI continues to evolve, advances in computational power, machine learning algorithms, and sensor technologies will likely allow us to overcome many of these limitations. However, it’s crucial for businesses and industries to set realistic expectations and recognise that AI video analytics, while powerful, still requires human oversight in many cases.

Conclusion

Through these cutting-edge strategies—self-learning neural networks, transfer learning, multi-network collaboration, adaptive learning, and edge computing—IntelexVision is successfully addressing the complex challenges posed by Moravec’s Paradox. By enabling AI systems to become more adept at interpreting and responding to visual data, IntelexVision is leading the way in transforming video analytics, making it smarter, faster, and more reliable across industries.

Author: rdadminzik

Date: 2024-09-07