In the field of physical security, effectively tackling the “Four-W” problem—Who, What, Where, and When—is essential for robust asset protection and safety. This framework prompts security professionals to identify potential threats, pinpoint valuable assets, assess vulnerabilities, and evaluate timing risks. The integration of iSentry, Intelexvision’s fourth-generation AI video analytics, can significantly enhance the ability to address these challenges.

To manage the “Who,” iSentry can analyse surveillance footage in real time, using advanced AI algorithms to detect unusual behaviour and identify unauthorised individuals. This system goes beyond traditional methods by:

 1. Who is involved or poses a threat?

– Advanced Recognition Features: Leveraging behavioural patterns to alert security personnel about suspicious individuals or actions.

– Continuous Learning: The AI adapts and learns from new data, continuously improving its accuracy and effectiveness in identifying potential threats.

 2. What needs protection?

Determining “What” involves identifying and prioritising the protection of both tangible and intangible assets. iSentry enhances asset security through:

– Real-time Monitoring: Continuously surveilling critical areas where high-value assets are located, ensuring that any unauthorised access or suspicious activities around these assets are immediately flagged.

– Automated Alerts: Sending instant notifications to security teams when potential threats to valuable assets are detected, allowing for swift response and intervention.

 3. Where are the vulnerabilities?

In addressing “Where,” iSentry aids in vulnerability assessment by providing comprehensive surveillance that covers all potential weak points within a facility. Its capabilities include:

– Coverage Optimisation: AI-driven analysis to optimise camera placement and angles, ensuring all critical and vulnerable areas are under surveillance. Installing new video analytics usually provides a good excuse to review the cameras.

– Perimeter Intrusion Detection: Automatically detecting any breaches at the perimeter or sensitive areas, enhancing the security of the entire premises.

 4. When are risks heightened?

The “When” aspect involves understanding the timing of potential threats, which iSentry manages through:

– Unusual Behaviour Detection: Utilising historical data and real-time analysis to predict when security risks are more likely to occur, based on patterns of activity at different times.

– Event-based Recording: Focusing resources on specific alerts identified as high risk, ensuring detailed footage is available for review.

Conclusion

By incorporating iSentry from Intelexvision into their security strategy, organisations can significantly advance their approach to solving the Four-W problem in physical security. This cutting-edge AI video analytics system not only enhances traditional security measures but also provides a proactive, intelligent solution capable of anticipating and neutralising threats before they materialise. The use of such advanced technology ensures that organisations can protect their assets more effectively and maintain a safer environment for everyone involved.

As AI-based video analytics continues to revolutionise industries, a hidden threat lurks in the shadows, compromising the accuracy and reliability of these systems. Catastrophic interference, also known as forgetting, occurs when neural networks forget previous knowledge during the learning process, leading to a decline in performance. In this article, I’ll explore the implications of catastrophic interference on AI-based video analytics and how IntelexVision can help mitigate this risk.

The problem: catastrophic interference in neural networks

Catastrophic interference occurs when a neural network is trained on new data, causing it to forget previously learned information. This phenomenon can lead to:

In AI-based video analytics in surveillance, catastrophic forgetting can result in:

The general impact on AI-based video analytics

Catastrophic interference can have severe consequences in various applications, not only CCTV, including for example:

The impact on anomaly detection

In anomaly detection, neural networks are constantly trained to identify unusual patterns and behaviours. However, this continuous training can exacerbate catastrophic interference, leading to:

As the network forgets previous knowledge, it becomes less effective at detecting anomalies, compromising the entire system.

The IntelexVision solution

IntelexVision offers a solution to mitigate catastrophic interference in its unusual behaviour detection:

This approach helps avoid catastrophic forgetting by:

Other techniques useful to mitigate the risk of catastrophic forgetting

Conclusion

Catastrophic interference poses a significant threat to AI-based video analytics, especially in solutions providing anomaly detection with continuous learning. IntelexVision offers solutions to mitigate this risk. By implementing matured products, you can ensure the accuracy and reliability of your AI-based video analytics systems. Don’t let forgetting hold you back – future-proof your AI today!

Learn more about IntelexVision’s solutions and how to overcome catastrophic interference in AI-based video analytics. Contact us to schedule a demo and ensure the accuracy and reliability of your AI systems.

In the realm of artificial intelligence (AI), an artificial hallucination, often referred to as confabulation or delusion, occurs when an AI produces inaccurate or deceptive data as if it were fact.

For example, if asked to create a financial report for a firm, a delusional chatbot might fabricate information such as the company’s revenue being $20 billion, seemingly without any basis in reality. These instances, similar to hallucinations in psychology, are termed “hallucinations.” One significant difference is that while AI hallucinations involve unreasonable responses or beliefs, human hallucinations typically involve incorrect perceptions. Some experts argue that the phrase “AI hallucination” unreasonably anthropomorphises computers.

During the AI boom and the rise of popular chatbots like ChatGPT, which are based on large language models (LLMs), the term “AI hallucination” gained popularity. Users expressed dissatisfaction with these chatbots’ tendency to incorporate random, implausible lies into their output. Some estimates suggest that chatbots may experience frequent hallucinations up to 27% of the time.

The term “generated content that is nonsensical or unfaithful to the provided source content” is frequently used in natural language processing to describe hallucinations. Hallucinations can be categorised in several ways. They are classified as intrinsic and extrinsic, respectively, based on whether the output conflicts with the source or cannot be confirmed by the source. They can also be classified as closed-domain or open-domain, respectively, based on whether the result contradicts the prompt.

Data-driven hallucinations occur due to source-reference divergence. When a model is trained on data that exhibits divergence between source and reference (target), it may be incentivised to produce language that lacks foundation and is not entirely true to the source.

It has been demonstrated that any flawed generative model taught to maximise training likelihood will statistically inevitably result in hallucinations; hence, active learning (such as reinforcement learning from human feedback) must be avoided. According to other research, hallucinations result from a conflict between novelty and use. An emphasis on novelty in machine creativity may result in the generation of unique but incorrect answers, or lies, while an emphasis on utility may result in rote memorisation of responses that are ineffective.

Hallucinations might result from mistakes made during the encoding and decoding of text and representations. An incorrect generation that departs from the input may occur when encoders pick up incorrect correlations between various elements of the training data. The final target sequence is produced by the decoder using the encoded input from the encoder. Hallucinations are caused by two parts of the decoding process. First, incorrect generation may result from decoders focusing on the incorrect portion of the encoded input source. Second, hallucinations may be exacerbated by the decoding strategy’s design. There is a positive correlation between increasing hallucinations and decoding strategies like top-k sampling that increase generation diversity.

In the realm of artificial intelligence (AI) and video analytics, advancements are continuously reshaping the landscape of surveillance, security, and data analysis. However, as AI systems become more sophisticated, there’s a growing need to address potential challenges, including the reflection of hallucinations in video analytics.

One potential avenue where hallucinations could influence AI video analytics is through the interpretation of ambiguous or incomplete visual data. AI algorithms rely on pattern recognition and machine learning to identify objects, behaviours, and anomalies within video feeds. However, in situations where the visual input is unclear or distorted, AI systems may generate erroneous interpretations, akin to hallucinations in humans.

Furthermore, the limitations of AI in understanding contextual information and cues could contribute to hallucination-like errors in video analytics. Without a comprehensive understanding of the broader context surrounding a scene, AI algorithms may misinterpret visual stimuli, leading to erroneous conclusions or false alarms.

IntelexVision addresses the challenge of hallucinations in AI video analytics through a comprehensive approach. Firstly, rigorous testing and validation procedures are implemented to assess the robustness of our AI models against ambiguous or distorted visual input. This involves subjecting the algorithms to diverse scenarios, including those that simulate hallucinatory effects, to identify and rectify vulnerabilities proactively. Additionally, our AI systems are equipped with context-awareness mechanisms to interpret visual data within the broader context of a scene, minimising the risk of misinterpretations. Continuous monitoring and feedback loops are integral to our process, enabling us to detect and correct deviations from expected behaviour promptly. By prioritising transparency and accountability in our development practices, IntelexVision ensures that our AI-driven video analytics solutions deliver reliable and trustworthy results, even in complex and dynamic environments.

In today’s rapidly evolving world, security and surveillance have become integral components of our daily lives. As technology continues to advance, the integration of Artificial Intelligence (#AI) into #CCTV systems has revolutionised the way we monitor and secure our surroundings. However, the question arises: where should AI processing take place in CCTV systems? The answer lies in the power of #edge architecture.

Before diving into why AI processing on edge architecture is superior in CCTV systems, let’s define what edge processing is. Edge processing, also known as edge computing, refers to the practice of performing data processing and analysis locally on a device or sensor, rather than relying on a centralised cloud server. In the context of CCTV systems, this means that the AI algorithms responsible for video analysis and event recognition are executed on the camera itself or on a nearby edge server, rather than sending all the data to a remote cloud server for analysis.

To illustrate the effectiveness of edge processing in event recognition, consider the following scenarios:

In addition to recognising specific predefined events, AI processing at the edge excels at identifying unusual behaviour patterns. This is where the self-learning capability of AI comes into play.

Imagine a CCTV system installed in a corporate office. Over time, the AI algorithms running on the edge devices become familiar with the typical patterns of activity, such as regular office hours and common employee movements. When an unusual event occurs, like someone attempting to access a restricted area during non-working hours, the AI can flag this behaviour as suspicious, even if it hasn’t been explicitly programmed to recognise that specific event.

Here’s how self-learning AI enhances event recognition:

In summary, the integration of self-learning AI in edge processing for event recognition takes surveillance to a whole new level. By allowing AI to adapt, learn, and identify unusual behaviours, we can enhance security, reduce false alarms, and stay ahead of emerging threats. As technology evolves, the self-learning capability of AI in CCTV systems on the edge is poised to play a pivotal role in creating safer and more efficient environments for both businesses and communities.

In conclusion, AI processing in CCTV systems is undoubtedly a game-changer, but the choice of architecture matters. Edge processing, with its low latency, bandwidth efficiency, and improved privacy and security, emerges as the superior option for event recognition. By pushing the AI processing closer to the source of data capture, we can create smarter, more responsive, and more secure surveillance systems that are better equipped to protect our communities and assets in today’s fast-paced world. As technology continues to advance, the edge is where the future of AI-powered CCTV lies.

A compute barrier in #video #AI analytics refers to a bottleneck or limitation in the computational resources available for processing and analyzing video data using artificial intelligence (AI) techniques. Video AI #analytics involves the use of machine learning and computer vision algorithms to extract valuable information and insights from video streams. These algorithms require significant computational power to perform tasks such as object detection, tracking, facial recognition, sentiment analysis, and more.

A compute barrier can manifest in several ways:

The hardware (e.g., CPUs, GPUs) available for running AI #algorithms may not be powerful enough to handle the workload efficiently. As a result, processing video data can be slow and less responsive.

Video data can be large and may not fit into the available memory, causing excessive data transfers between memory and storage, which can slow down processing.

If video streams are being processed over a network, high latency or limited bandwidth can create a compute barrier, as the AI system may not be able to receive and process data in real-time.

When dealing with large numbers of video streams, scaling the compute infrastructure to handle the load can be challenging, and resource limitations can hinder performance.

Some AI algorithms used in video analytics are computationally intensive. If the algorithms are too complex and not optimized, they can create a compute barrier, especially on less powerful hardware.

Overcoming compute barriers in video AI analytics often involves addressing these issues through a combination of strategies:

Increasing the processing power, memory, and storage capacity of the hardware can help handle the computational workload more effectively.

Distributing the workload across multiple processing units (e.g., GPUs or distributed computing clusters) can improve performance.

Optimizing algorithms and code for efficiency can reduce the computational requirements and improve real-time performance.

Implementing smart caching strategies and efficient data management techniques can reduce the need for frequent data transfers and improve efficiency.

Enhancing network infrastructure and reducing latency can help ensure that video data can be processed without significant delays.

In summary, a compute #barrier in video AI analytics refers to limitations in computational resources that hinder the efficient processing of video data using AI algorithms. Overcoming these barriers typically involves a combination of hardware upgrades, algorithm optimisation, and other strategies to ensure that the system can handle the workload effectively.

Can anything more be done now? You can. You can jump to the fourth generation of AI. Large AI models require huge hardware resources. Most often we take them as fixed, and unchangeable. And can the model be optimised? Most often, a neural network model especially based on deep learning is poorly modifiable, this is due to the assumptions of the metrics, and also the objective function. And are there other alternative models that are hardware-wise better suited to specific cases? It seems that there are. And what this 4th generation AI (#4genAI) is, I will explain in one of the next articles.