Video Analytics in Urban Environments

Hampapur, Arun; Bobbitt, Russell; Brown, Lisa M.; Desimone, Mike; Feris, Rogério; Kjeldsen, Rick; Lu, Max; Mercier, Carl; Milite, Chris; Russo, Stephen A.; Shu, Chiao-Fe; Zhai, Yun

doi:10.1109/avss.2009.100

Cited by 14 publications

(7 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Video analytics can be used in disaster risk management for accident cases and investigations, as well as disaster area identification and damage estimation [80]. 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

109

View full text Add to dashboard Cite

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.

show abstract

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

109

View full text Add to dashboard Cite

show abstract

“…These patches are then represented using appearance [13] and motion model, and the correspondences between inner and outer patches in successive frames are found to finally decide whether or not potential objects have crossed the LOI. This approach has shown an improved detection rate in dense traffic scenes as compared to tracker-based method [3][4][5]. However, in the two scenarios whereby one object appears in multiple patches and also objects with different appearance merged before the LOI and splitted after the LOI, there will be an ambiguity in finding the patches correspondences and consequently will affect the detection.…”

Section: Introductionmentioning

confidence: 99%

“…This application is frequently known as the virtual fence [2], virtual boundary [3] or tripwire [4][5]. In [3], virtual boundary or tripwire is defined as continuous lines or curves that are drawn on the image space to capture the directional crossing of the target objects.…”

Section: Introductionmentioning

confidence: 99%

Video analytics algorithm for detecting objects crossing lines in specific direction using blob-based analysis

Kadim

Meng

Samudin

et al. 2012

Proceedings of the 10th International Conference on Advances in Mobile Computing &Amp; Multimedia - MoMM '12

View full text Add to dashboard Cite

This paper presents a video analytics algorithm for detecting event of objects crossing predetermined line-of-interest in the scene in specific direction. A fast blob-based analysis is formulated to detect the event, combined with the object detection and tracking to detect and tracked the object as motion blobs. Proposed algorithm is tested in real outdoor surveillance environment for 24 hours in 3 days to evaluate the detection accuracies in different scenarios. For comparison, the testing is done against a commercial surveillance system. The results show that the proposed algorithm provides better accuracy in all scenarios, while maintaining real-time processing capacity.

show abstract

“…Video analytics is a technology using computer vision techniques that assists human operators to monitor surveillance video for suspicious behavior. It has been widely used in border security, transportation security, retail, financial and many other sectors [2,3]. The functionalities of video analytics systems include activity and object detection, target localization and tracking, event and behavior recognition and many application specific capabilities, e.g.…”

Section: Introductionmentioning

confidence: 99%

Mass casualty incident surveillance and monitoring using identity aware video analytics

Ganz

2010

2010 Annual International Conference of the IEEE Engineering in Medicine and Biology

View full text Add to dashboard Cite

In this paper, we propose an identity aware video analytic system that can assist securing the perimeter of a mass casualty incident scene and generate identity annotated video records for forensics and training purposes. Establishing a secure incident scene perimeter and enforcing access control to different zones is a demanding task for current video surveillance systems which lack the ability to provide the identity of the target and its security clearance. Our system which combines active RFID sensors with video analytic tools recovers the identity of the target enabling the activation of suitable alert policies. The system also enables annotation of incident scene video with identity metadata, facilitating the incident response process reconstruction for forensics analysis and emergency response training.

show abstract

Video Analytics in Urban Environments

Cited by 14 publications

References 4 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

Video analytics algorithm for detecting objects crossing lines in specific direction using blob-based analysis

Mass casualty incident surveillance and monitoring using identity aware video analytics

Contact Info

Product

Resources

About