Sailax is a global pioneer in providing various products and services that are aimed at enhancing people's lives. The AI-based video surveillance and event monitoring software are all you need to protect your home and workplace and be at ease. The swift procedure of video-based artificial intelligence and monitoring not only logs but proactively identifies issues and revolutionizes deep machine learning that evolves with your business.
A new research project in Australia is using motion detectors and muscle sensors to track sheep shearers in an effort to minimize on the-job-injuries. Sheep shearers are six times more likely to be injured in the workplace than the average Australian worker. Data from sensors attached to sheep shearers will be used to model worker movement throughout the workday and test new ways of doing the job without risking injury. The study, a joint project between University of Melbourne and the trade group Australian Wool Innovation, uses sensors to measure electrical activity in muscles. These sensors are placed directly on the skin of the lower back and upper thighs, the ABC reported, while motion detectors are placed around the joints to track a worker's posture and shearing motions.
Avigilon Corporation, a Motorola Solutions company, presents the newest version of its video management software, Avigilon Control Center (ACC) 7.4, which incorporates artificial intelligence-powered facial recognition technology. FEATURES OF AVIGILON CONTROL CENTER (ACC) 7.4 The new "appearance alerts" capability will help commercial organizations, such as educational institutions and hospitals, accelerate response times by identifying people of interest in enterprise settings. For example, the technology can alert the security team at a local high school when a banned or flagged individual has entered the campus. People of interest are identified based on a secure, controlled watch list created and maintained by authorized users at the commercial organization. For organizations that use the new ACC software and license their Avigilon cameras for facial recognition, cameras will seek to identify potential matches based on the watch list.
Here we are reviewing some of the major improvements that Light's rapid neural networks make over standard motion detection. We are building on the learnings from our previous article in which we compare Light with an IVS. Light has only gotten better since and this footage shows why. Light's rapid neural networks scan and understand the behavior of the people inside your security footage. The above video shows you what Light would see if you could "look through its eyes".
Probabilistic approaches to computer vision typically assume a centralized setting, with the algorithm granted access to all observed data points. However, many problems in wide-area surveillance can benefit from distributed modeling, either because of physical or computational constraints. Most distributed models to date use algebraic approaches (such as distributed SVD) and as a result cannot explicitly deal with missing data. In this work we present an approach to estimation and learning of generative probabilistic models in a distributed context where certain sensor data can be missing. In particular, we show how traditional centralized models, such as probabilistic PCA and missing-data PPCA, can be learned when the data is distributed across a network of sensors.
Learning to detect content-independent transformations from data is one of the central problems in biological and artificial intelligence. An example of such problem is unsupervised learning of a visual motion detector from pairs of consecutive video frames. Rao and Ruderman formulated this problem in terms of learning infinitesimal transformation operators (Lie group generators) via minimizing image reconstruction error. Unfortunately, it is difficult to map their model onto a biologically plausible neural network (NN) with local learning rules. Here we propose a biologically plausible model of motion detection.
In the wake of the May 2018 mass shooting that resulted in 10 deaths at Santa Fe (Texas) High School, the Santa Fe Independent School District looked at all possible options to improve school safety within reasonable financial constraints. The district considered the idea of technology to enhance its approximately 750 cameras with facial recognition but did not immediately see a workable solution -- for reasons of cost, and concerns about shaky accuracy that could lead to false positives, says Kip Robins, director of technology for Santa Fe ISD, which has about 4,500 students. The district ultimately contracted with a company called AnyVision, which demonstrated its Better Tomorrow product, an artificial-intelligence-based application that plugs into an existing camera network and provides the ability to do surveillance based on a certain face, body or object. School districts or other end users can create a watch list to keep an eye out for potential pedophiles, for example, or someone known to be mentally unstable. The Santa Fe ISD's solution is part of a growing cadre of software offerings that use artificial intelligence to power through reams of data and notice certain predetermined visual information – whether it's someone's face, or a certain license plate, or simply human movement in a place and time where there shouldn't be any.
The use of artificial intelligence, machine learning and robotics has enormous potential, but along with that promise come critical privacy and security challenges, says technology attorney Stephen Wu. For example, in healthcare, "we're beginning to see surgical robots ... and robots that take supplies from one part of a hospital to another. But along with those bold technological advances come emerging privacy and security concerns. "The HIPAA Security Rule doesn't talk about surgical robots and AI systems," he notes. Nevertheless, HIPAA's administrative, physical and technical safeguard requirements still apply, he says. As a result, organizations must determine, for example, "what kind of security management procedures are touching these devices and systems - and do you have oversight over them?" Also critical is ensuring that "communications are secure from one point to another," he points out. "If you have an AI system that's drawing records from an electronic health record, how is that transmission being secured?
Machine learning (ML) is shaping the cyberworld and how users interact with organizations. It has made incredible impacts on the anti-fraud industry and strengthened present security solutions. Learn what Cyxtera's machine learning experts are seeing now and predicting for the future of machine learning – and how the technology can be leveraged by both attackers and security teams. Adversarial machine learning is a technique in which algorithms are fed malicious input in an attempt to fool them into making analysis mistakes. Criminals looking to exploit algorithms can already use this technique to a limited extent, and Cyxtera's experts foresee adversarial machine learning being increasingly leveraged by criminals, making it more prevalent in the future of fraud attacks.
The Internet of things (IoT) has a significant potential to fall into the endless pit of a buzzword- vagueness, and it merely is an ecosystem of various kinds of objects that are connected through the Internet. These kinds of objects ranging from cell phones and wearables to machines, generate a constant and massive amount of data every day. The artificial intelligence (AI) also often falls into the same trap. The goal of artificial intelligence in the new IoT scenario is not only to use the humongous data to extract meaningful insights but also to help IoT integrated setups to derive higher value. It implies the machine's intelligence, where the device gains the capabilities of simulating a real human brain.