Video surveillance systems are evolving and are using artificial intelligence (AI) to inspect and analyse video footage, interpret patterns and flag unusual activity. Lenovo DCG and Pivot3 provide a state-of-the-art upgraded infrastructure solutions that aim to enhance current technology required to support these systems rather than entrusting the preservation of crucial data to outdated NVR technology. Commenting on the partnership, Dr. Chris Cooper, General Manager for Lenovo DCG, Middle East, Turkey and Africa, said, "We are delighted to showcase our partnership with Pivot3 at one the world's leading technology trade shows. The Middle East is exhibiting tremendous growth in terms of adopting smart solutions. The UAE in particular is investing heavily in implementing the latest innovations in their technological infrastructure; therefore, we see great potential from our partnership with Pivot3 as we work together to supply the appetite for next generation computing products and services."
Smart utility metering for power, gas and water, and video surveillance will remain by far the largest smart city segment, representing 87 per cent of the total number of smart city connections by 2026. This is according to new analysis by ABI Research. While metering is mainly focused on usage monitoring, savings and efficient operation of utility networks, video surveillance is no longer just about security and crime detection and prevention, ABI Research's Smart Cities market data report finds. Video surveillance is increasingly enabling new applications like urban tolling and the monitoring of low-emission zones to reduce air pollution, mainly in Europe. These systems use licence plate recognition to identify older vehicles banned from entering the zone.
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.
Hi! Just sharing with my recent project clever-camera which is a simple IP camera monitoring web service which uses MobileNet classifier to filter camera events based on the predicted labels - with possibility to search through the history of events or send email notifications in the case of camera movement detection. In practice CC uses MobileNetV3 to classify the content of selected ROIs, so one can filter events using predicted labels e.g. The application was written to work realtime ( 1 FPS) on Raspberry Pi 4 (in my case, I had low power consumption requirements), but it can be also run on a standard desktop/laptop with Ubuntu. You just need to have access to some IP camera to run monitoring. The whole application is just a few files and it's completely written in Python (thanks to remigui library), so it should be relatively easy for anyone to modify the code to e.g.
AI works to overcome the limitations of existing, antiquated database systems, through increasing automation and providing compliance management to its users. Furthermore, AI applications focus on productivity and efficiency; they determine the most effective communication method for each debtor, and use machine learning tools to predict and analyse customer behaviour (more on machine learning below). The overall impact is debt recovery and collection is streamlined as a process. The traditional process of debt collection via a human workforce can be incredibly labour intensive, and therefore expensive. Company's collection departments place calls, send emails manually, and manage accounts by updating databases by hand.
IT security teams today struggle to make sense of the enormous amounts of data modern IT infrastructures generate and consume, while simultaneously prioritizing and responding to alerts. This enormous responsibility is one of the reasons why detection and remediation times are so poor. In fact, a malicious attack has an average lifecycle of 314 days from breach to containment, according to the "2019 Cost of a Data Breach Report" by IBM. Manual and semiautomated checks and interventions cannot keep up with a constantly evolving threat landscape. And, with the average cost of a data breach estimated at $150 per record lost, according to the IBM study, a strong case can be made for automating many security tasks.
Microsoft's Windows Hello offers a few different authentication methods so you can sign into Windows 10 without using a conventional password. You can adopt a PIN, a physical security key, your fingerprint, or facial recognition. The facial recognition option can be handy as all you need do is glance at your computer's camera to authenticate yourself. But, only certain cameras support Windows Hello facial recognition, and you have to set up the feature for it to scan and recognize your face. On the plus side, if you're able to set up facial recognition, you can use it for more than just signing in to Windows 10.
During the 13 years he spent in banking, Tomasz Borowski's career spanned operations, risk management, and product management, where he also witnessed the brutality and unprofessional methods that conventional debt collection agencies adopted to retrieve outstanding balances. He observed the same thing when he moved to Ukraine in 2005 to continue his banking career. He did his own research on alternative methods and came to know about two digital debt collection companies--US-based TrueAccord and Polish debt collection agency Kruk SA--both worth over a billion US dollars today. "I decided to move to Southeast Asia and began exploring the market here. It was obvious to me that the situation here was similar to Ukraine. I decided to utilize my experience in finance and create a professional credit management services company to help change this market," Borowski told KrASIA.
A data leak by smart home device manufacturer Wyze left the personal details of 2.4 million users exposed on the internet for more than three weeks. Among the compromised information was user email addresses, WiFi network names, smart device details and the health statistics of a limited number of users. Founded by former Amazon employees, the Seattle, Washington-based firm specialises in inexpensive smart cameras, light bulbs, plugs and security devices. Wyze has now secured the database and forced users to reset their account passwords, as well as their connections with other services like Amazon's Alexa or Google assistant. A data leak by smart home device manufacturer Wyze left the personal details of 2.4 million users exposed on the internet for more than three weeks.