Graph coloring based surveillance video synopsis

He, Yi; Gao, Changxin; Sang, Nong; Qu, Zhiguo; Han, Ji

doi:10.1016/j.neucom.2016.11.011

Cited by 45 publications

(18 citation statements)

References 50 publications

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“…Video narratives [174] , set theoretical method [175] , spatial-temporal optimization [46] , background expanding [163] Reducing collision Object collision loss [47] , scaling-down objects [176] , speed-size incorporating [177] , graph model [45,178] Improving efficiency Pixel-based detection [179,180] , JAYA and simulated annealing [182] , online optimization [166,184] , compressed domain [185,186] Multi-camera video synopsis…”

Section: Improving Compressionmentioning

confidence: 99%

Video distillation

LI¹,

ZHAO²

2021

Sci. Sin.-Inf.

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Section: Improving Compressionmentioning

confidence: 99%

Video distillation

LI¹,

ZHAO²

2021

Sci. Sin.-Inf.

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“…Furthermore, it can be used in modelling and analysis of the complex applications. Some examples of GT in the computer vision domain are listed as: (1) graph-coloring-based surveillance video synopsis [89], (2) visual tracking using sparse representation combined with context information [90], two graph classes characterization by small forbidden induced structures using the weighted coloring problem [91], NP-complete problems solutions [92], users' mobility graph, and the computer vision applications such as representation and matching of categorical shape, and human activity recognition. These, and many others, are promising GT-based applications [93].…”

Section: Uses Of Graph Theory In a Computer Vision Domain/applicationsmentioning

confidence: 99%

Graph Theory: A Comprehensive Survey about Graph Theory Applications in Computer Science and Social Networks

Majeed

Rauf

2020

Inventions

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Graph theory (GT) concepts are potentially applicable in the field of computer science (CS) for many purposes. The unique applications of GT in the CS field such as clustering of web documents, cryptography, and analyzing an algorithm’s execution, among others, are promising applications. Furthermore, GT concepts can be employed to electronic circuit simplifications and analysis. Recently, graphs have been extensively used in social networks (SNs) for many purposes related to modelling and analysis of the SN structures, SN operation modelling, SN user analysis, and many other related aspects. Considering the widespread applications of GT in SNs, this article comprehensively summarizes GT use in the SNs. The goal of this survey paper is twofold. First, we briefly discuss the potential applications of GT in the CS field along with practical examples. Second, we explain the GT uses in the SNs with sufficient concepts and examples to demonstrate the significance of graphs in SN modeling and analysis.

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“…Some recent examples of a singleview are [275]- [277]. He et al [275], [276] brought advancement in activity collision analysis by describing collision statuses between activities such as collision-free, colliding in the same direction and opposite directions. They also offered a graph-based optimization technique by considering these collision states to improve the activity density and put activity collisions at the center of their optimization strategy.…”

Section: C: Video Synopsismentioning

confidence: 99%

Video Big Data Analytics in the Cloud: A Reference Architecture, Survey, Opportunities, and Open Research Issues

2020

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The proliferation of multimedia devices over the Internet of Things (IoT) generates an unprecedented amount of data. Consequently, the world has stepped into the era of big data. Recently, on the rise of distributed computing technologies, video big data analytics in the cloud has attracted the attention of researchers and practitioners. The current technology and market trends demand an efficient framework for video big data analytics. However, the current work is too limited to provide a complete survey of recent research work on video big data analytics in the cloud, including the management and analysis of a large amount of video data, the challenges, opportunities, and promising research directions. To serve this purpose, we present this study, which conducts a broad overview of the state-of-the-art literature on video big data analytics in the cloud. It also aims to bridge the gap among large-scale video analytics challenges, big data solutions, and cloud computing. In this study, we clarify the basic nomenclatures that govern the video analytics domain and the characteristics of video big data while establishing its relationship with cloud computing. We propose a service-oriented layered reference architecture for intelligent video big data analytics in the cloud. Then, a comprehensive and keen review has been conducted to examine cutting-edge research trends in video big data analytics. Finally, we identify and articulate several open research issues and challenges, which have been raised by the deployment of big data technologies in the cloud for video big data analytics. To the best of our knowledge, this is the first study that presents the generalized view of the video big data analytics in the cloud. This paper provides the research studies and technologies advancing the video analyses in the era of big data and cloud computing. INDEX TERMS big data, intelligent video analytics, cloud-based video analytics system, video analytics survey, deep learning, distributed computing, intermediate results orchestration, cloud computing.

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Graph coloring based surveillance video synopsis

Cited by 45 publications

References 50 publications

Video distillation

Video distillation

Graph Theory: A Comprehensive Survey about Graph Theory Applications in Computer Science and Social Networks

Video Big Data Analytics in the Cloud: A Reference Architecture, Survey, Opportunities, and Open Research Issues

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