Research Summary


Project: ITR - Multi-level, Active Attention Surveillance
Support: National Science Foundation (10/1/04 - )
Award Number: 0428249
Principal Investigators: R. Parent, R. Machiraju, J. Davis, D. Woods and A.T. Murray

Summary

This proposal seeks to advance security surveillance monitoring by introducing event-based reasoning. The team will use a formal event-discovery protocol to uncover event categories and the temporal structure of events. This results in an event template hierarchy. The event template hierarchy is supported by the enabling technologies of smart sensors, a reconfigurable network, and the use of persistent models for tracking. The result is an autonomous sensor network that can be effectively coupled to human operators in order to allow top-down control of the resources as well as the ability to modify the models for event and background activities. While the methodology is suitable for a wide variety of application domains, the work is grounded in a campus security and surveillance paradigm. By integrating research from Cognitive Science, Geography, and Computer Science (Graphics, and Vision), the team can create a paradigmatic shift in the way that surveillance systems are viewed and developed. The data stream is no longer composed merely of video and perhaps some low-level alarms; the focus is now extended to include events. Data and information no longer move toward a usersitting in front of a wall of monitors. Event contexts, set by higher-level events as well as by operators-in-the-loop, direct and focus attention in order to detect differences from a dynamic model of background activity. The result is that the information is more meaningful, the surveillance systems more focused, and the cognitive skills of the operators more efficiently utilized. A prototype system will be made available for pertinent security personnel to train and test. The work will contribute to training methodologies of security personnel. Under the purview of broader impact, the proposed work strives to include under-represented and minority student groups through targeted training in the use of video technology. Finally if successful, event-based strategic surveillance networks can provide alternatives to racial profiling. The individuals are judged only by their actions as encoded in the event models.

 

Figure 1. Monitoring area (Ohio State University)

 

Figure 2. Area coverage maximization

 

Figure 3. Backup coverage maximization

 

Student Collaborators

Kamyoung Kim - Ph.D. student, Department of Geography, OSU

Produced Publications

  1. "Coverage optimization to support security monitoring," A.T. Murray, K. Kim, J.W. Davis, R. Machiraju and R. Parent, Computers Environment and Urban Systems, to appear.
  2. "A multi-objective evolutionary algorithm for surveillance sensor placement," K. Kim, A.T. Murray and N. Xiao, submitted for publication.

Presentations

  1. “Coverage optimization to support security monitoring,” A.T. Murray, K. Kim, J. Davis, R. Machiraju and R. Parent, 101st Annual Meeting of the Association of American Geographers, Denver, Colorado, USA, April 5-9, 2005.
  2. "Modeling to support greater integration of surveillance system sensors," A.T. Murray and K. Kim, 52nd North American Meeting of the Regional Science Association International, Las Vegas, Nevada, USA, November 10-12, 2005.
  3. "Modeling to support 24/7 security monitoring," K. Kim and A.T. Murray, 102nd Annual Meeting of the Association of American Geographers, Chicago, Illinois, USA, March 7-11, 2006.
  4. "A multi-objective evolutionary algorithm for surveillance sensor placement," K. Kim, A.T. Murray and N. Xiao, 53rd North American Meeting of the Regional Science Association International, Toronto, Ontario, Canada, November 16-18, 2006
Last updated : 1/11/07