The Intelligent vision sensor: Turning video into information

Lipton, A.J.; Clark, John I.; Thompson, Brad; Myers, Gregory; Titus, Swathy; Zhong, Zhang; Venetianer, P.L.

doi:10.1109/avss.2007.4425287

Cited by 10 publications

(4 citation statements)

References 2 publications

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“…In the case of smart real estate, video analytics can be used for threat detection, security enhancements, and surveillance [81]. Applications such as the Intelligent Vision Sensor turn video imagery into actionable information that can be used in building automation and business intelligence applications [82].…”

Section: Video Analyticsmentioning

confidence: 99%

Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis

Munawar

Qayyum

Ullah

et al. 2020

BDCC

115

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Big data is the concept of enormous amounts of data being generated daily in different fields due to the increased use of technology and internet sources. Despite the various advancements and the hopes of better understanding, big data management and analysis remain a challenge, calling for more rigorous and detailed research, as well as the identifications of methods and ways in which big data could be tackled and put to good use. The existing research lacks in discussing and evaluating the pertinent tools and technologies to analyze big data in an efficient manner which calls for a comprehensive and holistic analysis of the published articles to summarize the concept of big data and see field-specific applications. To address this gap and keep a recent focus, research articles published in last decade, belonging to top-tier and high-impact journals, were retrieved using the search engines of Google Scholar, Scopus, and Web of Science that were narrowed down to a set of 139 relevant research articles. Different analyses were conducted on the retrieved papers including bibliometric analysis, keywords analysis, big data search trends, and authors’ names, countries, and affiliated institutes contributing the most to the field of big data. The comparative analyses show that, conceptually, big data lies at the intersection of the storage, statistics, technology, and research fields and emerged as an amalgam of these four fields with interlinked aspects such as data hosting and computing, data management, data refining, data patterns, and machine learning. The results further show that major characteristics of big data can be summarized using the seven Vs, which include variety, volume, variability, value, visualization, veracity, and velocity. Furthermore, the existing methods for big data analysis, their shortcomings, and the possible directions were also explored that could be taken for harnessing technology to ensure data analysis tools could be upgraded to be fast and efficient. The major challenges in handling big data include efficient storage, retrieval, analysis, and visualization of the large heterogeneous data, which can be tackled through authentication such as Kerberos and encrypted files, logging of attacks, secure communication through Secure Sockets Layer (SSL) and Transport Layer Security (TLS), data imputation, building learning models, dividing computations into sub-tasks, checkpoint applications for recursive tasks, and using Solid State Drives (SDD) and Phase Change Material (PCM) for storage. In terms of frameworks for big data management, two frameworks exist including Hadoop and Apache Spark, which must be used simultaneously to capture the holistic essence of the data and make the analyses meaningful, swift, and speedy. Further field-specific applications of big data in two promising and integrated fields, i.e., smart real estate and disaster management, were investigated, and a framework for field-specific applications, as well as a merger of the two areas through big data, was highlighted. The proposed frameworks show that big data can tackle the ever-present issues of customer regrets related to poor quality of information or lack of information in smart real estate to increase the customer satisfaction using an intermediate organization that can process and keep a check on the data being provided to the customers by the sellers and real estate managers. Similarly, for disaster and its risk management, data from social media, drones, multimedia, and search engines can be used to tackle natural disasters such as floods, bushfires, and earthquakes, as well as plan emergency responses. In addition, a merger framework for smart real estate and disaster risk management show that big data generated from the smart real estate in the form of occupant data, facilities management, and building integration and maintenance can be shared with the disaster risk management and emergency response teams to help prevent, prepare, respond to, or recover from the disasters.

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Section: Video Analyticsmentioning

confidence: 99%

Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis

Munawar

Qayyum

Ullah

et al. 2020

BDCC

115

View full text Add to dashboard Cite

show abstract

“…developing the so-called distributed intelligence solution. This trend has resulted in recent emergence of various DSP-based analytics embedded in cameras [14], video severs, storages [10], devices [18]. Besides, the distributed video surveillance system architecture and design will be radically different compared to the known centralised system.…”

Section: The Open Issues For Video Analytics Practitionersmentioning

confidence: 99%

“…et al from ObjectVideo[18] report their work on "The intelligent vision sensor: Turning video into information". This…”

mentioning

confidence: 99%

Issues in video analytics and surveillance systems: Research / prototyping vs. applications / user requirements

2007

2007 IEEE Conference on Advanced Video and Signal Based Surveillance

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The next generation video surveillance system will be a networked, intelligent, multi-camera co-operative system with integrated situation awareness of dynamic and complex scenes, be it a wide area like an urban centre or indoor complex like a shopping mall. The core of such a system is the increasingly intelligent and robust video analytics that are capable of analysing the videos from low-level image appearance and feature extraction to midlevel object / event detection to high-level reasoning and scene understanding. While great strides have been made in researching the issues underlying the above capabilities by academia and industry labs over the last decade, the race is now on the applications of these in integrated systems, providing automated solutions for realistic surveillance problems. This panel discussion paper attempts to outline some of the issues arising from such a transformation, the way forward towards meeting end users' needs, and the aspects of on-going industry R&D activities. Questions are raised as to how to bridge the gap between the video analytics research and system applications and solutions in the market place.

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“…Privacy invasion is always a critical issue in setting up visual sensors around living spaces because of the danger of revealing video data from image sensors or processors [4]. These issues exist with the above-mentioned solutions because of the inevitability of inputting or outputting video data.…”

Section: Introductionmentioning

confidence: 99%

iVisual

Cheng

Lin

et al. 2008

Proceedings of the 45th Annual Design Automation Conference

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iVisual, an intelligent visual sensor SoC integrating 2790fps CMOS image sensor and 76.8GOPS, 374mW vision processor, is implemented on a 7.5mm×9.4mm die with 0.18µm CIS process. The light-in, answer-out SoC architecture avoids privacy problems of intelligent visual sensor. The feature processor and inter-processor synchronization scheme together increase 51% of average throughput. The 205GOPS/W power efficiency and 1.16GOPS/mm 2 area efficiency are achieved.

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The Intelligent vision sensor: Turning video into information

Cited by 10 publications

References 2 publications

Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis

Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis

Issues in video analytics and surveillance systems: Research / prototyping vs. applications / user requirements

iVisual

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