Cover Image

Camera networks : the acquisition and analysis of videos over wide areas /

Bibliographic Details
Main Author: Roy-Chowdhury, Amit K.
Other Authors: Song, Bi, Ph. D.
Format: eBook
Language:English
Published: San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool, [2012]
Series:Synthesis lectures on computer vision ; #4.
Subjects:
Video surveillance > Computer networks.
Video recording > Computer networks.
TECHNOLOGY & ENGINEERING / Electrical.
wide area tracking
distributed video analysis
Kalman consensus
distributed tracking
recognition
active sensing
opportunistic sensing
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • 1. An introduction to camera networks
  • 1.1 Research directions
  • 1.1.1 Camera network topology
  • 1.1.2 Wide area tracking
  • 1.1.3 Distributed processing
  • 1.1.4 Camera network control (active vision)
  • 1.1.5 Mobile camera networks
  • 1.1.6 Simulation in camera networks
  • 1.1.7 Experimental testbeds
  • 1.1.8 Application domains
  • 1.2 Organization of the book
  • 2. Wide-area tracking
  • 2.1 Review of multi-target tracking approaches
  • 2.1.1 Kalman filter-based tracker
  • 2.1.2 Particle filter-based tracker
  • 2.1.3 Multi-hypothesis tracking (MHT)
  • 2.1.4 Joint probabilistic data association filters (JPDAF)
  • 2.2 Tracking in a camera network - problem formulation
  • 2.3 A review on camera network tracking
  • 2.4 On-line learning using affinity models
  • 2.5 Tracklet association using stochastic search
  • 2.6 Person reidentification
  • 2.7 Learning a camera network topology
  • 2.8 Consistent labeling with overlapping fields of view
  • 2.9 Conclusions
  • 3. Distributed processing in camera networks
  • 3.1 Consensus algorithms for distributed estimation
  • 3.2 Decentralized and distributed tracking
  • 3.2.1 Decentralized tracking
  • 3.2.2 Distributed tracking
  • 3.3 Consensus algorithms for distributed tracking
  • 3.3.1 Mathematical framework
  • 3.3.2 Extended kalman-consensus filter for a single target
  • 3.3.3 JPDA-EKCF for tracking multiple targets
  • 3.3.4 Handoff in consensus tracking algorithms
  • 3.3.5 Example of distributed tracking using EKCF
  • 3.3.6 Sparse networks and naive nodes - the generalized Kalman Consensus filter
  • 3.4 Camera network calibration
  • 3.4.1 Distributed data association
  • 3.4.2 Distributed calibration
  • 3.4.3 Distributed pose estimation
  • 3.5 Conclusions
  • 4. Object and activity recognition
  • 4.1 Object recognition
  • 4.1.1 Object recognition under resource constraints
  • 4.2 Time-delayed correlation analysis
  • 4.2.1 Scene decomposition and activity representation
  • 4.2.2 Cross canonical correlation analysis
  • 4.2.3 Applications
  • 4.3 Activity analysis using topic models
  • 4.3.1 Probabilistic model
  • 4.3.2 Labeling trajectories into activities
  • 4.4 Distributed activity recognition
  • 4.4.1 Consensus for activity recognition
  • 4.5 Conclusions
  • 5. Active sensing
  • 5.1 Problem formulation
  • 5.1.1 Active sensing of dynamical processes
  • 5.2 Review of existing approaches
  • 5.3 Collaborative sensing in distributed camera networks
  • 5.3.1 System modeling
  • 5.3.2 Distributed optimization framework
  • 5.3.3 Choice of utility functions
  • 5.3.4 Negotiation mechanisms
  • 5.3.5 Example scenarios
  • 5.3.6 Results in example scenarios
  • 5.4 Opportunistic sensing
  • 5.4.1 Global utility
  • 5.4.2 Experiments
  • 5.5 Conclusions
  • 6. Future research directions
  • Authors' biographies.