International Data Corporation (IDC) recently published an IDC Innovators report profiling five companies that offer compelling and differentiated computer vision software. The five companies are Algolux, Deep Vision AI, Sighthound, ViSenze, and Umbo CV. Computer vision is an AI technology that allows computers to understand and label images. Use cases include video surveillance, driverless car testing, daily medical diagnostics, and monitoring the health of crops and livestock. AI is used for pattern recognition and learning techniques driven largely by machine learning (ML) and deep learning (DL) algorithms that bring visual understanding capabilities in a growing variety of hardware and software applications.
Intelligent transportation systems (ITSs) will be a major component of tomorrow's smart cities. However, realizing the true potential of ITSs requires ultra-low latency and reliable data analytics solutions that can combine, in real-time, a heterogeneous mix of data stemming from the ITS network and its environment. Such data analytics capabilities cannot be provided by conventional cloud-centric data processing techniques whose communication and computing latency can be high. Instead, edge-centric solutions that are tailored to the unique ITS environment must be developed. In this paper, an edge analytics architecture for ITSs is introduced in which data is processed at the vehicle or roadside smart sensor level in order to overcome the ITS latency and reliability challenges. With a higher capability of passengers' mobile devices and intra-vehicle processors, such a distributed edge computing architecture can leverage deep learning techniques for reliable mobile sensing in ITSs. In this context, the ITS mobile edge analytics challenges pertaining to heterogeneous data, autonomous control, vehicular platoon control, and cyber-physical security are investigated. Then, different deep learning solutions for such challenges are proposed. The proposed deep learning solutions will enable ITS edge analytics by endowing the ITS devices with powerful computer vision and signal processing functions. Preliminary results show that the proposed edge analytics architecture, coupled with the power of deep learning algorithms, can provide a reliable, secure, and truly smart transportation environment.
"Computer systems can automatically detect and interpret what is happening on video surveillance cameras; Siri allows anyone to have a personal assistant in their pocket; Watson has beaten two former champions on Jeopardy and Google driverless cars have driven over 500 000km accident-free. Modern technology is increasingly intelligent," says Suren Govender, Accenture Analytics MD. With the growing availability of sensors, better algorithms for data analytics and growing computational power, these intelligent technologies are becoming more prevalent and are being incorporated in everyday life and business. "The cognitive era is about thinking itself – how we gather information, access it and make decisions," notes Hamilton Ratshefola, country GM at IBM South Africa. Cognitive analytics engines have the ability to build knowledge and learn, they understand natural language, reason and interact more naturally with human beings than traditional programmable systems.
The democratization of machine learning (ML) has led to ML-based machine vision systems for autonomous driving, traffic monitoring, and video surveillance. However, true democratization cannot be achieved without greatly simplifying the process of collecting groundtruth for training and testing these systems. This groundtruth collection is necessary to ensure good performance under varying conditions. In this paper, we present the design and evaluation of Satyam, a first-of-its-kind system that enables a layperson to launch groundtruth collection tasks for machine vision with minimal effort. Satyam leverages a crowdtasking platform, Amazon Mechanical Turk, and automates several challenging aspects of groundtruth collection: creating and launching of custom web-UI tasks for obtaining the desired groundtruth, controlling result quality in the face of spammers and untrained workers, adapting prices to match task complexity, filtering spammers and workers with poor performance, and processing worker payments. We validate Satyam using several popular benchmark vision datasets, and demonstrate that groundtruth obtained by Satyam is comparable to that obtained from trained experts and provides matching ML performance when used for training.
The concept of "smart cities" is no longer confined to the realms of futuristic science fiction--they're quickly becoming part of our everyday reality. Technologies like self-driving buses that communicate with traffic lights and AI-monitored CCTV cameras are being implemented in cities from Singapore to Las Vegas, and the technology behind these smart-city initiatives promises innovative solutions for both municipalities and their citizens--offering safer and more efficient living for an ever-growing population. The smart-city promise is often delivered without the fine print though: namely, that a single attack waged against just one component of a connected infrastructure could disable an entire smart city in a matter of minutes. The attack could come from a single line of code. This looming threat is turning the promises of revolutionized living standards into a potential menace to public safety.