Localization Using Low-Resolution Optical Sensors

Massey, Tammara; Kapur, Radhika V.; Dabiri, Foad; Vu, Linh Nam; Sarrafzadeh, Majid

doi:10.1109/mobhoc.2007.4428621

Cited by 6 publications

(5 citation statements)

References 19 publications

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“…There are some algorithms to extract the target from the video frames, such as the brightest pixel identification algorithm, the circular filter algorithm, and the two-dimensional convolution algorithm. By comparisons, the accuracy of the target extraction of the circular filter algorithm is better than others' according to the works of Massey [19]. Therefore, we use the Canny detector, one kind of circular filter algorithms, to determining the target edge.…”

Section: Statistical Methods Of Target Localizationmentioning

confidence: 99%

Improving target localization accuracy of wireless visual sensor networks

Portilla

Moreno

et al. 2011

IECON 2011 - 37th Annual Conference of the IEEE Industrial Electronics Society

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This paper discusses the target localization problem of wireless visual sensor networks. Specifically, each node with a low-resolution camera extracts multiple feature points to represent the target at the sensor node level. A statistical method of merging the position information of different sensor nodes to select the most correlated feature point pair at the base station is presented. This method releases the influence of the accuracy of target extraction on the accuracy of target localization in universal coordinate system. Simulations show that, compared with other relative approach, our proposed method can generate more desirable target localization's accuracy, and it has a better trade-off between camera node usage and localization accuracy.

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Section: Statistical Methods Of Target Localizationmentioning

confidence: 99%

Improving target localization accuracy of wireless visual sensor networks

Portilla

Moreno

et al. 2011

IECON 2011 - 37th Annual Conference of the IEEE Industrial Electronics Society

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“…Unfortunately, the target's extraction and localization are always corrupted by measurement noises and errors in practice. Massey et al (2007) proposes methods to implement target localization using camera networks. They discussed two methods of triangulation for determining a target's position in the global coordinate space, grid based coordination and convex polygon intersection scheme.…”

Section: Related Workmentioning

confidence: 99%

Multiple feature points representation in target localization of wireless visual sensor networks

Portilla

Moreno

et al. 2015

Journal of Network and Computer Applications

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a b s t r a c tThis paper discusses the target localization problem in wireless visual sensor networks. Additive noises and measurement errors will affect the accuracy of target localization when the visual nodes are equipped with low resolution cameras. In the goal of improving the accuracy of target localization without prior knowledge of the target, each node extracts multiple feature points from images to represent the target at the sensor node level. A statistical method is presented to match the most correlated feature point pair for merging the position information of different sensor nodes at the base station. Besides, in the case that more than one target exists in the field of interest, a scheme for locating multiple targets is provided. Simulation results show that, our proposed method has desirable performance in improving the accuracy of locating single target or multiple targets. Results also show that the proposed method has a better trade off between camera node usage and localization accuracy.

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“…We can project the overlap extent of cameras' FOVs to the overlap area of intersections of the sensing model of the cameras. This intersection is a series of triangle intersections; therefore, it will always be a convex polygon [23]. Utilizing the method of computing the union of two convex polygons, which is described in [24], we can distinguish the intersection region and order its vertices.…”

Section: Correlations Between Imagesmentioning

confidence: 99%

Camera Sensor Activation Scheme for Target Tracking in Wireless Visual Sensor Networks

2013

International Journal of Distributed Sensor Networks

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Target tracking has become an elementary task in wireless visual sensor networks (WVSNs). In this paper, we propose a camera sensor activation scheme for target tracking in WVSN. Our objective is to balance the tradeoff between the accuracy of target tracking and the resources of networks. By studying the sensing model and deployments of cameras, an observation correlation coefficient is derived to describe the correlation characteristics of visual information observed by cameras with overlapped field of views. According to the observation correlation coefficient, a correlation-based camera activation scheme is designed. Experimental results show that the proposed observation correlation coefficient can model the correlation characteristics of visual information in WVSN. Further simulations show that the correlation-based camera activation scheme has satisfied performance in target tracking compared with other sensor selection methods.

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Localization Using Low-Resolution Optical Sensors

Cited by 6 publications

References 19 publications

Improving target localization accuracy of wireless visual sensor networks

Improving target localization accuracy of wireless visual sensor networks

Multiple feature points representation in target localization of wireless visual sensor networks

Camera Sensor Activation Scheme for Target Tracking in Wireless Visual Sensor Networks

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