Sensor selection for improving accuracy of target localisation in wireless visual sensor networks

Li, W.; Zhang, W.

doi:10.1049/iet-wss.2012.0033

Cited by 22 publications

(16 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They introduce artificial pheromones to adapt to changes in the network. Li and Zhang [30] use a utility based on the number of cameras observing a target and on the overlapping area of the target to determine which cameras should collaborate to achieve the best results. This is done exhaustively for all cameras in a centralised way.…”

Section: Collaboration and Team Workmentioning

confidence: 99%

The Future of Camera Networks

Esterle

Lewis

McBride³

et al. 2017

Proceedings of the 11th International Conference on Distributed Smart Cameras

View full text Add to dashboard Cite

Camera networks become smart when they can interpret video data on board, in order to carry out tasks as a collective, such as target tracking and (re-)identification of objects of interest. Unlike today's deployments, which are mainly restricted to lab settings and highly controlled high-value applications, future smart camera networks will be messy and unpredictable. They will operate on a vast scale, drawing on mobile resources connected in networks structured in complex and changing ways. They will comprise heterogeneous and decentralised aggregations of visual sensors, which will come together in temporary alliances, in unforeseen and rapidly unfolding scenarios. The potential to include and harness citizen-contributed mobile streaming, body-worn video, and robotmounted cameras, alongside more traditional fixed or PTZ cameras, and supported by other non-visual sensors, leads to a number of difficult and important challenges. In this position paper, we discuss a variety of potential uses for such complex smart camera networks, and some of the challenges that arise when staying smart in the presence of such complexity. We present a general discussion on the challenges of heterogeneity, coordination, self-reconfigurability, mobility, and collaboration in camera networks. KEYWORDSfuture camera networks, surveillance, mobile smart cameras, body worn cameras, humans in the loop, distributed control ACM Reference format:

show abstract

Section: Collaboration and Team Workmentioning

confidence: 99%

The Future of Camera Networks

Esterle

Lewis

McBride³

et al. 2017

Proceedings of the 11th International Conference on Distributed Smart Cameras

View full text Add to dashboard Cite

show abstract

“…Centralized optimization maximizes functions that quantify target location accuracy [6] [17][9] or visibility [10]. Such optimization is often restricted to small-size networks as its explores all the combinations of cameras to form a coalition [6] [17].…”

Section: Related Workmentioning

confidence: 99%

“…Such optimization is often restricted to small-size networks as its explores all the combinations of cameras to form a coalition [6] [17]. Greedy approaches reduce such complexity by sequentially expanding the coalition only with cameras maximizing certain criteria [9][10].…”

Section: Related Workmentioning

confidence: 99%

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

SanMiguel

Cavallaro

2015

IEEE Sensors J.

View full text Add to dashboard Cite

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.

show abstract

“…WSNs have gained popularity because they have a variety of uses in different applications, such as target tracking [1,2], localization [3], healthcare [4,5], Smart Transpiration [6], environmental monitoring, and industrial automation [7]. WSN is also used in collecting data and monitoring, both autonomously or with the help of users [8,9].…”

Section: Introductionmentioning

confidence: 99%

IoT-Based Intelligent Modeling of Smart Home Environment for Fire Prevention and Safety

Saeed

Paul

Rehman

et al. 2018

JSAN

165

View full text Add to dashboard Cite

Fires usually occur in homes because of carelessness and changes in environmental conditions. They cause threats to the residential community and may result in human death and property damage. Consequently, house fires must be detected early to prevent these types of threats. The immediate notification of a fire is the most critical issue in domestic fire detection systems. Fire detection systems using wireless sensor networks sometimes do not detect a fire as a consequence of sensor failure. Wireless sensor networks (WSN) consist of tiny, cheap, and low-power sensor devices that have the ability to sense the environment and can provide real-time fire detection with high accuracy. In this paper, we designed and evaluated a wireless sensor network using multiple sensors for early detection of house fires. In addition, we used the Global System for Mobile Communications (GSM) to avoid false alarms. To test the results of our fire detection system, we simulated a fire in a smart home using the Fire Dynamics Simulator and a language program. The simulation results showed that our system is able to detect early fire, even when a sensor is not working, while keeping the energy consumption of the sensors at an acceptable level.

show abstract

Sensor selection for improving accuracy of target localisation in wireless visual sensor networks

Cited by 22 publications

References 10 publications

The Future of Camera Networks

The Future of Camera Networks

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

IoT-Based Intelligent Modeling of Smart Home Environment for Fire Prevention and Safety

Contact Info

Product

Resources

About