|
|
|
| Target localization method based on wireless multimedia sensor network |
| LI Jian-ting, JIN Xin-yu, TANG Jun, ZHANG Yu |
| Department of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China |
|
|
|
Abstract A single node and double nodes target localization method used in simplified calibration scenes was proposed based on the characteristics of wireless multimedia sensor network (WMSN) to deal with multinode cooperation. The pixel coordinate of the special point was gained through using the auxiliary calibration object with color to mark the position of special node and using the Camshift algorithm to trace the color of auxiliary calibration object. The location of special node in monitor area was used to obtain the parameters of the camera matrix. Threedimensional coordinates of moving targets were obtained through arranging tow nodes which have the characteristic of translation. Experimental results showed that the largest error of single node plane localization was 12.16 cm, the largest error of double nodes plane localization was 13.69 cm, and the error of double nodes height localization was 3-5 cm. The error surface of twodimensional plane shows that the localization error of the most nodes can meet the needs of WMSN target localization with low complexity and simple implementation．
|
|
Published: 03 March 2011
|
|
|
基于无线多媒体传感器网络的目标定位方法
根据无线多媒体传感器网络(WMSN)多节点合作处理的特点,提出简化定标场景的单节点和双节点目标定位方法.通过使用带颜色的辅助定标物标定特殊点位置,采用CamShift算法实现辅助标定物的颜色追踪,获得特殊点的像素坐标.利用节点监控区域特殊点的位置坐标获取摄像机的参数矩阵,通过布置2个具有平移特性的节点,获取运动目标的三维坐标.实验表明,单节点平面定位最大误差为1216 cm,双节点平面定位最大误差为1369 cm,双节点高度定位误差为3～5 cm.通过观察二维平面误差曲面,大多数节点的定位误差能够满足无线多媒体传感器网络目标跟踪的需要,算法复杂度低,实现过程简单.
|
|
| ［1］ AKYILDIZ I.F, MELODIA T, CHOWDURY K R. A survey on wireless multimedia sensor networks ［J］. Computer Networks, 2007, 51(14): 921-960．
［2］ HE T, YANG X, CHEN K. Research on target tracking of binocular vision robot ［J］. Machinery Design and Manufacture, 2008, 3(3): 161-163．
［3］ ZHANG Z. A flexible new technique for camera calibration ［J］. IEEE Transaction Pattern Analysis and Machine Intelligence, 2000, 22(11): 1330-1334．
［4］ BRADSKI G R, CLARA S. Computer vision face tracking for use in a perceptual user interface ［J］. Intelligence Technology Journal, 1998(14): 1-15．
［5］ FUKUNAGA K. Introduction to statistical pattern recognition ［M］. Boston: Academic Press, 1990: 400-435．
［6］ YU Y. Research on videobased traffic detection system and camera calibration algorithm ［D］. Tianjin: Tianjin University, 2007: 39-41．
［7］ MA S, ZHANG Z. Computer vision: compute theory and basic of algorithm ［M］. Beijing: Science Press, 2003: 18-34．
［8］ YILMAZ A, JAVED O, SHAH M. Object tracking: a survey ［J］. ACM Computing Surveys, 2006, 38(4): 1-45．
［9］ LIU R, YU S. OpenCV tutorial: basics ［M］. Beijing: Beijing University of Aeronautics and Astronautics Press, 2007: 178-382．
［10］ JIANG Zhiqiong, GUO Wei. On energy issues of cooperative communication in energy constrained wireless networks ［C］∥Global Telecommunications Conference. San Francisco: ［s.n.］, 2006: 1-5．
［11］ WANG Fubao, SHI Long, REN Fengyuan. Selflocalization systems and algorithms for wireless sensor networks ［J］. Journal of Software, 2005, 16(5): 857-868． |
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
| |
Shared |
|
|
|
|
| |
Discussed |
|
|
|
|
