Dynamic Node Collaboration for Mobile Target Tracking in Wireless Camera Sensor Networks Liang Liu†,‡, Xi Zhang†, and Huadong Ma‡ † Networking and Information System Laboratory Department of Electrical and Computer Engineering Texas A&M University, College Station, TX 77843, USA ‡ Beijing Key Lab of Intelligent Telecomm. Software and Multimedia, Beijing University of Posts and Telecomm., Beijing 100876, China Study group 3/10 Jason
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Dynamic Node Collaboration for Mobile Target Tracking in Wireless Camera Sensor Networks Liang Liu†,‡, Xi Zhang†, and Huadong Ma‡ † Networking and Information.
Introduction 2 advantages of camera tracking 1. Most informative sensor 2.High-level analysis using image processing 2 characteristics of camera tracking 1. Cooperative localization algorithm 2. Forming a dynamic cluster 1)Electing of cluster head(CH) 2)Selecting of cluster members(Cm)
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Dynamic Node Collaboration for Mobile Target Tracking in Wireless
Camera Sensor NetworksLiang Liu†,‡, Xi Zhang†, and Huadong Ma‡
† Networking and Information System LaboratoryDepartment of Electrical and Computer Engineering
Texas A&M University, College Station, TX 77843, USA‡ Beijing Key Lab of Intelligent Telecomm. Software and Multimedia,
Beijing University of Posts and Telecomm.,Beijing 100876, China
Study group 3/10 Jason
Outline
• Introduction• Motion model of the target• Sensing model of camera sensors• Target tracking by SMC• Dynamic Node Collaboration• Selection of the Cluster Member• Simulation Results
Introduction
• 2 advantages of camera tracking1. Most informative sensor 2. High-level analysis using image processing• 2 characteristics of camera tracking 1. Cooperative localization algorithm2. Forming a dynamic cluster2. Forming a dynamic cluster
1) Electing of cluster head(CH) 2) Selecting of cluster members(Cm)
Introduction1) Electing of cluster head (CH)2) Selecting of cluster members(Cm)1) Electing of cluster head (CH)
- SMC based tracking procedure2) Selecting of cluster members(Cm)- Optimization-based algorithm
(considering about accuracy and energy consumption)
Motion model of the target
Target location
Target velocity
Sensing model of camera sensors
Li: Location of camerasr: Radius of sensingVi: unit vector of Alpha: offset angle
Sensing model of camera sensors
Background Subtraction
Sensing model of camera sensors
Target tracking by SMC
• Tracking problem consists– Posterior function– Estimating location by expectation
• At t-1, we’ve known
Target tracking by SMC
Posterior function updating
Estimating location by expectation
Dynamic Node Collaboration
Motion Model of Target
Selection of Cluster Member• Maximum utility: maximize the localization
accuracy under the specified cost
• Minimum cost: minimize the cost so as to attain a specified accuracy