http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
the acquisition and analysis of videos over wide areas
Synthesis digital library of engineering and computer science
1 online resource (xiii, 119 pages) : illustrations
As networks of video cameras are installed in many applications like security and surveillance, environmental monitoring, disaster response, and assisted living facilities, among others, image understanding in camera networks is becoming an important area of research and technology development. There are many challenges that need to be addressed in the process.
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.
Part of: Synthesis digital library of engineering and computer science.
Includes bibliographical references (pages 105-118).
Synthesis lectures on computer vision ; #4