Case Study

Feris, Rogério; Hampapur, Arun; Zhai, Yun; Bobbitt, Russell; Brown, Lisa M.; Vaquero, Daniel; Tian, Yingli; Liu, Haowei; Sun, Ming–Ting

doi:10.1201/9781439813300-c3

Cited by 6 publications

(3 citation statements)

References 12 publications

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“…Subsequent approaches based on intelligent video analytics addresses this situation with sophisticated computer vision algorithms. As the area under surveillance increases, performance issues related to scalability, bandwidth, security, the volume of multimedia data, and risk of a single point of failure [6], prompt an extension or replacement of centralised approaches by distributed strategies [5, 8, 22, 23]. Additionally, the presence of concurrent recognition events, inefficient multimedia data management techniques, and lack of synchronisation associated with the recognition, reasoning, and retrieval mechanisms could dampen the performance of video analytics solutions operating in wide‐area [8].…”

Section: Related Workmentioning

confidence: 99%

“…Thus the ubiquitous presence of visual sensors in public and private places can form a distributed surveillance system that allows spatio–temporal situation awareness. However, such systems face various challenges caused by redundancy in multimedia data [23]. Song et al .…”

Section: Related Workmentioning

confidence: 99%

“…Thus the ubiquitous presence of visual sensors in public and private places can form a distributed surveillance system that allows spatio-temporal situation awareness. However, such systems face various challenges caused by redundancy in multimedia data [23]. Song et al [24] discussed the distributed tracking using a Kalman consensus filtering, involving hand-off between neighbouring autonomous cameras as the target moves from the field of view of one camera to the next.…”

Section: Camera Network For Identification and Trackingmentioning

confidence: 99%

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Modelling large scale camera networks for identification and tracking: an abstract framework

Mohan¹,

Menon²

2020

IET Computer Vision

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In this study, the authors discuss a novel approach for multi‐camera‐based unobtrusive identification and tracking of occupants in wide‐area, multi‐building scenarios. Considering the scalability issues in adopting a centralised approach to monitor wide‐area scenarios, they proposed a distributed approach to occupant identification and tracking. The key technical idea underlying their approach is to abstract a wide‐area indoor surveillance environment using a distributed state transition system (DSTS) model, which in turn is composed of independent building‐specific state transition systems, coordinating and collaborating with each other. This study presents the details of their DSTS model and examines the temporal ordering of recognition events within the DSTS for ensuring accurate state information and responses to spatio–temporal queries. They also provide an experimental evaluation of the performance of their model using precision‐recall metrics. Their conclusion is that the DSTS model serves as an efficient mechanism for tracking occupants in wide‐area, multi‐building scenarios monitored by camera networks.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Camera Network For Identification and Trackingmentioning

confidence: 99%

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Modelling large scale camera networks for identification and tracking: an abstract framework

Mohan¹,

Menon²

2020

IET Computer Vision

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Scalable Spatio-temporal Analysis on Distributed Camera Networks

Hong

Ottenwälder

Ramachandran

2014

Studies in Computational Intelligence

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Technological advances and the low cost of sensors enable the deployment of large-scale camera networks in airports and metropolises. A wellknown technique, called spatio-temporal analysis, enables detecting anomalies such as an individual entering into a restricted area without permission. Spatio-temporal analysis requires a large amount of system resources to infer locations of occupants in real-time. In particular, state update becomes a bottleneck due to computation and communication overhead to update possibly large application state. In this paper we propose a system design and mechanisms for scalable spatio-temporal analysis. We present a distributed system architecture including smart cameras and distributed worker nodes in the cloud to enable real-time spatio-temporal analysis on large-scale camera networks. Furthermore we propose and implement a couple of selective update mechanisms to further improve scalability of our system by reducing the communication cost for state update.

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