Machine learning (ML) is a field within Artificial Intelligence (AI) and one of the more common buzzwords in the physical security market. ML focuses on building computer systems that can learn and improve on their own, without being explicitly programmed for every scenario. Machine learning is poised to revolutionise physical security by offering a more proactive, data-driven approach to securing people and assets. We asked this week’s Expert Panel Roundtable: What is Machine Learning (ML) and how can it benefit physical security?
Video analytics have been around for years, but since 2009, the feasibility of deep learning has led to successive waves of intelligent features. By training neural networks, basic analytics tasks like motion detection, object detection, and tracking objects within scenes became commonplace. Further advances in machine learning are driving the current progress in artificial intelligence (AI), empowering systems to learn and enhance through experience, without explicitly relying on programming. This is made possible by training solutions that derive insights from video and other data, uncovering hidden patterns and knowledge. Machine learning is revolutionising security by making it possible to automate the detection of potentially harmful patterns and swiftly identify breaches or threats in real-time. Moreover, it can continue to learn over time from these incidents, enabling the prediction and potential prevention of future security violations. By effectively "learning" to discern risks, AI solutions can elevate the effectiveness and efficiency of security measures.
Machine learning (ML) involves teaching a machine to use inputs and historical information to improve its performance without being explicitly programmed to do so. Programmers use machine learning algorithms and datasets to train computers to recognise patterns, make predictions, and continuously improve their performance as they’re exposed to more data. This technology has a wide range of applications in the physical security industry and is particularly useful for repetitious, task-based analysis. For instance, at Genetec, we use ML in our automatic licence plate recognition system. The system takes an image of the back of a vehicle it hasn’t seen before and outputs the licence plate characters, along with data such as location, vehicle colour, and type. This is achieved by comparing the image with an ML model that was trained on labeled images and calculating the probability that the image belongs to a specified set of classifications.
Machine learning (ML) is a subset of artificial intelligence (AI) that allows computers to build their logic for making predictions and determinations. Without ML, a computer requires human input to create algorithms. Furthermore, deep learning (DL) is a refined version of ML that can learn to extract and combine features in a data-driven manner. DL is suitable for complex challenges such as image classification, language processing, and object detection, allowing it to be used for applications beyond traditional security like monitoring occupancy and time spent in an area. These intelligent technologies have helped to transform physical security from reactive to proactive, enabling organisations across industries to effectively tackle security threats head-on. This is thanks to automated, real-time analytic capabilities, which can dig into the granular details of captured footage, audio, etc. without human intervention saving valuable time and resources and allowing for faster incident resolution.
Machine learning (ML), a subset of artificial intelligence (AI), focuses on the development of algorithms and models that enable devices to perform tasks without specific instructions. ML models learn from training data (the dataset) that is provided to them. An ML model recognises patterns and relationships within the data it is given and outputs a resulting analysis very quickly. For physical security use cases involving video, ML provides an enormous benefit by automating certain surveillance tasks beyond what limited human resources can provide. ML can detect objects it has been trained to recognise (vehicles,humans) and describe the unique attributes (colour, type) associated with them making forensic search exceptionally fast and accurate. ML-based analytics can also provide anomaly detection alerting staff when an environment deviates from the norm. ML algorithms that run “on the edge” offer enhanced threat detection and proactive decision-making for security personnel in real time.
Machine learning is a name for computer programs that automatically estimate which action to take based on a set of input data. An example is whether or not to send a technician to clean a camera lens based on the current image of the camera and analyse if the view is clear or not. Machine learning can benefit physical security by automating certain rote, time-consuming but important tasks. For example, a machine learning program is well-suited to identify all cameras that appear to be tilted from their original position. This information can be supplied to an operator who can decide the best remedial action.
Machine Learning (ML) is a subfield of Artificial Intelligence (AI), focusing on the development of algorithms and models that enable computers to learn from data and make predictions or decisions. Machine learning systems are not explicitly programmed to perform specific tasks, but learn from the data provided, allowing computers to improve their performance on specific tasks as they encounter more data. The key point of machine learning is its ability to recognise patterns, learn these patterns, and make decisions or predictions without explicit human intervention. Machine Learning (ML) has several benefits for physical security systems, enhancing surveillance, threat detection, and access control. Here are some benefits of machine learning for physical security: Anomaly Detection: Machine learning algorithms can learn what is considered normal, and then detect anomalies or suspicious activities. Video Surveillance and Object Recognition: Machine learning algorithms can analyse video information to recognise and track objects, faces, and behaviours. Real-Time Threat Response: Machine learning algorithms integrated with security systems can quickly analyse and respond to potential threats. They can trigger alarms or responses based on predefined patterns or anomalous activities.