Collaboratively Target Localization Based on Vision for Wireless Multimedia Sensor Networks

Zhang, Bo; Luo, Haiyong; Liu, Jiwei; Zhao, Fang

doi:10.1166/sl.2011.1555

Cited by 2 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, LSSVR can obtain better location effect by optimizing the parameters of LSSVR. Hence, LSSVR algorithm has the unique advantage in the node localization for WSN [9]. For instance, the WSN node localization method based on the LSSVR modeling was proposed by Guixiong et al [10].…”

Section: Introductionmentioning

confidence: 99%

Mobile node localization method based on KF-LSSVR algorithm

Zhang

Wang

et al. 2018

J Wireless Com Network

View full text Add to dashboard Cite

As the mobile node localization algorithm in three-dimensional environment cannot meet the demand of actual application, a hybrid mobile node localization algorithm for a wireless sensor network (WSN) in three-dimensional environment is proposed in this paper, which is based on the least squares support vector regression (LSSVR) and Kalman filter (KF). The proposed algorithm firstly constructs the LSSVR localization model by sampling measurement area and training sample sets. Then, the KF model is used to iterate and correct the measured distance in order to obtain the distance between the unknown node and each anchor node. Finally, the LSSVR localization model is employed to obtain the estimated location of the unknown node. The experiments were conducted and the experimental results were analyzed according to ranging errors, anchor node density, communication radius, moving speed, and node localization errors. Simulation results show that the proposed algorithm using a joint KF and LSSVR algorithm is superior to the KF algorithm and the LSSVR algorithm, and it can reduce the localization errors and improve the localization accuracy.

show abstract

Section: Introductionmentioning

confidence: 99%

Mobile node localization method based on KF-LSSVR algorithm

Zhang

Wang

et al. 2018

J Wireless Com Network

View full text Add to dashboard Cite

show abstract

“…In WMSNs, video localization of targets is divided into localization with a single camera [ 11 , 12 ] and localization with collaborative cameras [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ]. It is usually required to obtain the height of target in advance [ 11 ] or to extract target perfectly [ 12 ] for localization with single camera.…”

Section: Introductionmentioning

confidence: 99%

“…Video collaborative localization of targets is achieved through fusing the video information collected and processed by multiple cameras. For example, reference [ 13 ] proposed a visual collaborative localization algorithm for the passive target positioning problem. According to machine vision theory, the target was projected into several straight lines on the ground plane.…”

Section: Introductionmentioning

confidence: 99%

The Video Collaborative Localization of a Miner’s Lamp Based on Wireless Multimedia Sensor Networks for Underground Coal Mines

You

Yang

Han

2015

Sensors

View full text Add to dashboard Cite

Based on wireless multimedia sensor networks (WMSNs) deployed in an underground coal mine, a miner’s lamp video collaborative localization algorithm was proposed to locate miners in the scene of insufficient illumination and bifurcated structures of underground tunnels. In bifurcation area, several camera nodes are deployed along the longitudinal direction of tunnels, forming a collaborative cluster in wireless way to monitor and locate miners in underground tunnels. Cap-lamps are regarded as the feature of miners in the scene of insufficient illumination of underground tunnels, which means that miners can be identified by detecting their cap-lamps. A miner’s lamp will project mapping points on the imaging plane of collaborative cameras and the coordinates of mapping points are calculated by collaborative cameras. Then, multiple straight lines between the positions of collaborative cameras and their corresponding mapping points are established. To find the three-dimension (3D) coordinate location of the miner’s lamp a least square method is proposed to get the optimal intersection of the multiple straight lines. Tests were carried out both in a corridor and a realistic scenario of underground tunnel, which show that the proposed miner’s lamp video collaborative localization algorithm has good effectiveness, robustness and localization accuracy in real world conditions of underground tunnels.

show abstract

Collaboratively Target Localization Based on Vision for Wireless Multimedia Sensor Networks

Cited by 2 publications

References 0 publications

Mobile node localization method based on KF-LSSVR algorithm

Mobile node localization method based on KF-LSSVR algorithm

The Video Collaborative Localization of a Miner’s Lamp Based on Wireless Multimedia Sensor Networks for Underground Coal Mines

Contact Info

Product

Resources

About