Visible to the public CPS- Synergy-Collaborative Research-Extracting time-critical situational awareness from resource constrained networks

Overall Objective. The goal of this project is to facilitate the timely retrieval of dynamic
situational awareness information from field deployed nodes by an operational center in
disaster recovery or search and rescue missions, which are typically characterized by
resource-constrained uncertain environments. Efficient situational awareness information
retrieval under severe resource limitations is critical in applications like disaster response.
Technology advances allow the deployment of field nodes capable of returning rich
content (e.g., video/images) that can significantly aid rescue and recovery. However,
development of techniques for acquisition, processing and extraction of the content that is
relevant to the operation under resource constraints poses significant interdisciplinary
challenges, which this project will address. The focus of the project will be on the
fundamental science behind these tasks. The research will hinge on sound scientific
reasoning facilitated by strong experimentation on testbeds available at UCR and UCI.
Research Contributions. Towards realizing a networked system that facilitates the
retrieval of time-critical, operation-relevant situational awareness, we have focused on
the following research tasks.
Task A: Resource-Constrained Data Acquisition and Analysis. This task looks at
how to reconfigure the network and adapt video analysis in real time to meet different
(sometimes conflicting) application requirements, given resource constraints. We have
specifically worked on active sensing in a network of cameras [1], and on adaptive
algorithm selection based on the environmental conditions and available resources [2].
Task B: Information Fusion Under Resource Constraints. This task proposes methods
to locally process and fuse the content generated, given the query needs and resource
constraints. It also considers how to summarize the content received in response to the
queries to facilitate further analysis at the operation center. In the first year, we have
specifically focused on human detection in bandwidth constrained networks [3].
Task C: Cost-effective Query Formulation and Retrieval. This task addresses
challenges in query formulation, refinement and retrieval, including (i) prioritizing
queries as per importance criteria, (ii) effective query dissemination in the field, and (iii)
effective retrieval of the sensed information. In the first year, we have focused on querydriven
clustering and tagging of faces in videos [4].
[1] C. Ding, J. H. Bappy, J. A. Farrell, A. Roy-Chowdhury, Opportunistic Image Acquisition of
Individual and Group Activities in a Distributed Camera Network, IEEE Transactions on Circuits
and Systems for Video Technology, 2016.
[2] S. Zhang, Q. Zhu, A. K. Roy-Chowdhury, "Adaptive Algorithm selection, with applications in
pedestrian detection", ICIP 2016.
[3] T. Dao, A. K. Roy-Chowdhury, N. Nasrabadi, S. V. Krishnamurthy, P. Mohapatra, L. Kaplan,
"ACTION: Accurate and Timely Situation Awareness Retrieval from Bandwidth Constrained
Wireless Cameras", submitted to IPSN 2017.
[4] L. Zhang, X. Wang, D. V. Kalashnikov, S. Mehrotra and D. Ramanan, "Query-Driven
Approach to Face Clustering and Tagging," in IEEE Transactions on Image Processing, vol. 25,
no. 10, pp. 4504-4513, Oct. 2016.

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