Ella: Middleware for multi-camera surveillance in heterogeneous visual sensor networks

Dieber, Bernhard; Simonjan, Jennifer; Esterle, Lukas; Rinner, Bernhard; Nebehay, Georg; Pflugfelder, Roman; Fernández, Gustavo J.

doi:10.1109/icdsc.2013.6778223

Cited by 11 publications

(4 citation statements)

References 13 publications

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“…The camera cluster consists of four atom-based cameras (1.6 GHz processor, 2 GB RAM, 30 GB internal SSD hard disk) from SLR Engineering and two PandaBoards on which the middle-ware system ELLA [40] is developed. The cameras in the cluster can perform object detection and tracking together with state estimation locally.…”

Section: Resultsmentioning

confidence: 99%

Multi-camera object tracking using surprisal observations in visual sensor networks

Bhuvana

Schranz

Regazzoni

et al. 2016

EURASIP J. Adv. Signal Process.

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In this work, we propose a multi-camera object tracking method with surprisal observations based on the cubature information filter in visual sensor networks. In multi-camera object tracking approaches, multiple cameras observe an object and exchange the object's local information with each other to compute the global state of the object. The information exchange among the cameras suffers from certain bandwidth and energy constraints. Thus, allowing only a desired number of cameras with the most informative observations to participate in the information exchange is an efficient way to meet the stringent requirements of bandwidth and energy. In this paper, the concept of surprisal is used to calculate the amount of information associated with the observations of each camera. Furthermore, a surprisal selection mechanism is proposed to facilitate the cameras to take independent decision on whether their observations are informative or not. If the observations are informative, the cameras calculate the local information vector and matrix based on the cubature information filter and transmit them to the fusion center. These cameras are called as surprisal cameras. The fusion center computes the global state of the object by fusing the local information from the surprisal cameras. Moreover, the proposed scheme also ensures that on average, only a desired number of cameras participate in the information exchange. The proposed method shows a significant improvement in tracking accuracy over the multi-camera object tracking with randomly selected or fixed cameras for the same number of average transmissions to the fusion center.

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Section: Resultsmentioning

confidence: 99%

Multi-camera object tracking using surprisal observations in visual sensor networks

Bhuvana

Schranz

Regazzoni

et al. 2016

EURASIP J. Adv. Signal Process.

Self Cite

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“…The Ella middleware [52] supports the definition of distributed analytics on the edge and over a WAN. They use a publish-subscribe model with hierarchical brokers to route video and event streams between analytics deployed on edge devices.…”

Section: Related Workmentioning

confidence: 99%

“…In general, this reflects our design philosophy. Today's video surveillance landscape offers either a high degree of flexibility [52,55], which increases the effort required to build application, or are too restrictive, limiting their flexibility [18,19]. Our domain-specific approach targets tracking applications, and offer developers an intuitive but customizable pattern for defining their tracking application, while our runtime is sensitive to the performance challenges of distributed computing that will affect this application class.…”

Section: Related Workmentioning

confidence: 99%

A Scalable Platform for Distributed Object Tracking across a Many-camera Network

Khochare¹,

Aravindhan²,

Simmhan³

2019

Preprint

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Advances in deep neural networks (DNN) and computer vision (CV) algorithms have made it feasible to extract meaningful insights from large-scale deployments of urban cameras. Tracking an object of interest across the camera network in near real-time is a canonical problem. However, current tracking frameworks have two key limitations: 1) They are monolithic, proprietary, and lack the ability to rapidly incorporate sophisticated tracking models; and 2) They are less responsive to dynamism across wide-area computing resources that include edge, fog and cloud abstractions. We address these gaps using Anveshak, a runtime platform for composing and coordinating distributed tracking applications. It provides a domain-specific dataflow programming model to intuitively compose a tracking application, supporting contemporary CV advances like query fusion and re-identification, and enabling dynamic scoping of the camera-network's search space to avoid wasted computation. We also offer tunable batching and data-dropping strategies for dataflow blocks deployed on distributed resources to respond to network and compute variability. These balance the tracking accuracy, its real-time performance and the active camera-set size. We illustrate the concise expressiveness of the programming model for 4 tracking applications. Our detailed experiments for a network of 1000 camera-feeds on modest resources exhibit the tunable scalability, performance and quality trade-offs enabled by our dynamic tracking, batching and dropping strategies.

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“…The distributed publish-subscribe middleware system Ella [3] is used to facilitate this distributed tracking application on heterogeneous hardware. Since the application runs upon the middleware Ella, all modules are realized as publishers and/or subscribers.…”

Section: The Demonstratormentioning

confidence: 99%

Demo

Simonjan

Esterle

Rinner

et al. 2014

Proceedings of the International Conference on Distributed Smart Cameras

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We have developed a self-aware multi-camera multi-object tracking application for distributed heterogeneous smart camera networks. This application is able to track multiple persons through the network whereby the cameras are able to transfer the tracking responsibility between each other in a self-organizing manner without any central control. This application runs on top of a fully distributed middleware, designed for extensibility and robustness as well as heterogeneous hardware and network technologies. Our demonstrator has been deployed in a laboratory environment consisting of different hardware platforms and network technologies.

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Ella: Middleware for multi-camera surveillance in heterogeneous visual sensor networks

Cited by 11 publications

References 13 publications

Multi-camera object tracking using surprisal observations in visual sensor networks

Multi-camera object tracking using surprisal observations in visual sensor networks

A Scalable Platform for Distributed Object Tracking across a Many-camera Network

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