Video Analytics for Business Intelligence

Hakeem, Asaad; Gupta, Himaanshu; Kanaujia, Atul; Choe, Tae Eun; Gunda, Kiran; Scanlon, Andrew; Yu, Li; Zhang, Zhong; Venetianer, P.L.; Rasheed, Zeeshan; Haering, Niels

doi:10.1007/978-3-642-28598-1_10

Cited by 16 publications

(7 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition to their high cost, labor-based surveillance systems tend to be less effective than automatic systems (e.g., Hakeem et al, 2012 report that security personnel cannot remain focused on surveillance tasks for more than 20 minutes). Video analytics can efficiently and effectively perform surveillance functions such as detecting breaches of restricted zones, identifying objects removed or left unattended, detecting loitering in a specific area, recognizing suspicious activities, and detecting camera tampering, to name a few.…”

Section: Video Analyticsmentioning

confidence: 98%

Beyond the hype: Big data concepts, methods, and analytics

Gandomi

Haider

2015

International Journal of Information Management

3,316

1,689

View full text Add to dashboard Cite

Section: Video Analyticsmentioning

confidence: 98%

Beyond the hype: Big data concepts, methods, and analytics

Gandomi

Haider

2015

International Journal of Information Management

3,316

1,689

View full text Add to dashboard Cite

“…One is traditional labor‐based video analysis, which is high cost and less effective (e.g., Hakeem et al . report that personnel cannot remain focused on surveillance tasks for more than 20 min). The other way of analyzing video content is based on Big Data technologies, which includes a variety of smart algorithms (e.g., video motion detection, video tracking, and ego‐motion estimation) to analyze and extract meaningful information from real time as well as prerecorded video streams efficiently and effectively.…”

Section: Illustrative Examplementioning

confidence: 99%

“…There are two ways to conduct this comparative analysis. One is traditional laborbased video analysis, which is high cost and less effective (e.g., Hakeem et al [38] report that personnel cannot remain focused on surveillance tasks for more than 20 min). The other way of analyzing video content is based on Big Data technologies, which includes a variety of smart algorithms (e.g., video motion detection, video tracking, and egomotion estimation) to analyze and extract meaningful After the analyzing the video content through an algorithm employing Big Data, nitrocellulose (C 12 H 16 N 4 O 18 ) was identified as the source of the fire, because it can burn spontaneously in that condition and its flame characteristics (as shown in Figures 5b and 6b) is consistent with the surveillance video and photos of the accident site (as shown in Figures 5a and 6a).…”

Section: Illustrative Examplementioning

confidence: 99%

A new paradigm for accident investigation and analysis in the era of big data

Huang

Wang

et al. 2017

Process Safety Progress

View full text Add to dashboard Cite

The advent of the era of Big Data has spawned a new research paradigm and has transformed the outlook of numerous fields in science and engineering. Similarly, as one of the important fields of safety science, the area of accident investigation also has great opportunities for leveraging Big Data to its advantage. With this in mind, in this article, the influencing factors of accident investigation were analyzed. Then, the definition of Safety‐related Big Data (SRBD) was analyzed, and a four‐layer pyramidal structure consisting of SRBD, Safety Information, Safety Law, and Safety Knowledge was constructed. Based on this, the conceptual model of the accident investigation paradigm based on SRBD was proposed. Moreover, the opportunities offered by the proposed new paradigm were argued from three aspects, which are “tools,” “inputs,” and “constraints.” Last, the proposed paradigm was applied in a case study. Results show that the proposed paradigm can provide a novel method for accident investigation and analysis. The presented paper aims to explore the great expectations for accident investigation in the era of Big Data. © 2017 American Institute of Chemical Engineers Process Process Saf Prog 37:42–48, 2018

show abstract

“…[3], [4], [5], [6], [7], [8]. However, it got an urgent and spread interest due to the global emergency.…”

Section: Introductionmentioning

confidence: 99%