Cluster-Based Distributed Face Tracking in Camera Networks

Yoder, Josiah; Medeiros, Henry; Park, Johnny; Kak, Avinash C.

doi:10.1109/tip.2010.2049179

Cited by 20 publications

(12 citation statements)

References 56 publications

(51 reference statements)

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“…Even though communication latency is targeted by handling relevant data across cameras, the amount of data produced can still overwhelm the network if a sound communication strategy is not available. Similar to our work clustering has been proposed in [30] as a means to reduce the amount of messaging in a network of collaborative cameras. The method differs, however, to our approach in that it is limited to face recognition, while we target collaborative tracking with a combination of hardware acceleration and middleware.…”

Section: Related Workmentioning

confidence: 90%

Efficient network clustering for traffic reduction in embedded smart camera networks

Zarezadeh¹,

Bobda

Mefenza

2015

J Real-Time Image Proc

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In this work, a clustering approach for bandwidth reduction in distributed smart camera networks is presented. Properties of the environment such as camera positions and environment pathways, as well as dynamics and features of targets are used to limit the flood of messages in the network. To better understand the correlation between camera positioning and pathways in the scene on one hand and temporal and spatial properties of targets on the other hand, and to devise a sound messaging infrastructure, a unifying probabilistic modeling for object association across multiple cameras with disjointed view is used. Communication is efficiently handled using a taskoriented node clustering that partition the network in different groups according to the pathway among cameras, and the appearance and temporal behavior of targets. We propose a novel asynchronous event exchange strategy to handle sporadic messages generated by non-frequent tasks in a distributed tracking application. Using a Xilinx-FPGA with embedded Microblaze processor, we could show that, with limited resource and speed, the embedded processor was able to sustain a high communication load, while performing complex image processing computations.

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Section: Related Workmentioning

confidence: 90%

Efficient network clustering for traffic reduction in embedded smart camera networks

Zarezadeh¹,

Bobda

Mefenza

2015

J Real-Time Image Proc

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“…Coalition formation requires identifying the best cameras over time. Moreover, collaboration costs of forming and operating camera coalitions are generally not defined [11] [13][7][9] [14], thus limiting the use of existing approaches in resource-constrained camera networks.…”

Section: Sanmiguel and Andrea Cavallaromentioning

confidence: 99%

“…Greedy approaches reduce such complexity by sequentially expanding the coalition only with cameras maximizing certain criteria [9][10]. Centralized approaches need Decentralized rankings select top-ranked cameras based on camera-target distance [11] or feature matching such as orientation [13]. Distance-based criteria require accurate target state estimations to create coalitions whereas feature-based criteria rely on matching accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…Instead, the proposed approach only estimates camera utilities and employs local interaction without requiring any global data. Unlike the homogeneous collaborative processing [11] [13], we weight coalition collaboration based on cameras' utilities. We propose a framework to model utilities and costs of camera coalitions, whereas related works in camera networks only describe costfree coalitions [ …”

Section: Related Workmentioning

confidence: 99%

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Cost-Aware Coalitions for Collaborative Tracking in Resource-Constrained Camera Networks

SanMiguel

Cavallaro

2015

IEEE Sensors J.

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Esta es la versión de autor del artículo publicado en: This is an author produced version of a paper published in: El acceso a la versión del editor puede requerir la suscripción del recurso Access to the published version may require subscriptionCost-aware coalitions for collaborative tracking in resource-constrained camera networks Juan C. SanMiguel and Andrea CavallaroAbstract-We propose an approach to create camera coalitions in resource-constrained camera networks and demonstrate it for collaborative target tracking. We cast coalition formation as a decentralized resource allocation process where the best cameras among those viewing a target are assigned to a coalition based on marginal utility theory. A manager is dynamically selected to negotiate with cameras whether they will join the coalition and to coordinate the tracking task. This negotiation is based not only on the utility brought by each camera to the coalition but also on the associated cost (i.e. additional processing and communication). Experimental results and comparisons using simulations and real data show that the proposed approach outperforms related state-of-the-art methods by improving tracking accuracy in costfree settings. Moreover, under resource limitations the proposed approach controls the tradeoff between accuracy and cost, and achieves energy savings with only a minor reduction in accuracy.

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“…Research in this field has been focused on distributed tracking, resource allocation, activity recognition and active sensing. Yoder et al [43] track multiple faces in a wireless camera network. The observations of multiple cameras are integrated using a minimum variance estimator and tracked using a Kalman filter.…”

Section: Multi-view-based Recognitionmentioning

confidence: 99%

Robust Face Recognition From Multi-View Videos

Sankaranarayanan

Chellappa

2014

IEEE Trans. on Image Process.

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Abstract-Multi-view face recognition has become an active research area in the last few years. In this paper, we present an approach for video-based face recognition in camera networks. Our goal is to handle pose variations by exploiting the redundancy in multi-view video data. However, unlike traditional approaches that explicitly estimate the pose of the face, we propose a novel feature for robust face recognition in the presence of diffuse lighting and pose variations. The proposed feature is developed using the spherical harmonic representation of the face texture-mapped onto a sphere; the texture map itself is generated by back-projecting the multi-view video data. Video plays an important role in this scenario. First, it provides an automatic and efficient way for feature extraction. Second, the data redundancy renders the recognition algorithm more robust. We measure the similarity between feature sets from different videos using the Reproducing Kernel Hilbert Space. We demonstrate that the proposed approach outperforms traditional algorithms on a multi-view video database.

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Cluster-Based Distributed Face Tracking in Camera Networks

Cited by 20 publications

References 56 publications

Efficient network clustering for traffic reduction in embedded smart camera networks

Efficient network clustering for traffic reduction in embedded smart camera networks

Cost-Aware Coalitions for Collaborative Tracking in Resource-Constrained Camera Networks

Robust Face Recognition From Multi-View Videos

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