dan schonfeld co-director, multimedia communications laboratory professor, departments of ece, cs...
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Nokia Research CollaborationGrammars, and Tensors
Dan Schonfeld
Co-Director, Multimedia Communications LaboratoryProfessor, Departments of ECE, CS & BioengineeringUniversity of Illinois at Chicago
© 2010 Board of Trustees of the University of Illinois
Picture/Illustration
Project Title: Distributed Multi-Target TrackingInvestigator(s): Dan Schonfeld
Problem StatementThe aim of this project is to provide a fast distributed multi-target tracking algorithm that integrates visual information from multiple cameras using a complex graphical model for the representation of object and camera interactions. The goal is to reduce the current exponential computational complexity associated with multi-target multi-camera tracking to a linear complexity by using a distributed approach.
Technical Approach
Key Achievements•Previous work on distributed multi-target tracking;•Previous work on distributed multi-camera tracking;•Previous work on complex graphical models;•Previous work resulted in linear computational complexity (objects);•Previous work on video tracking supported by NSF and industry;•Incorporated in industrial demos and other products.
ROM and ScheduleMulti-target, multi-camera tracking C/C++ codeEstimated Cost: $150K
Products and Deliverables•Multi-target, multi-camera tracking;•Complex graphical models;•Linear computational complexity (targets and cameras).
Contact Info: Dan Schonfeld, Univ. Illinois at Chicago, 851 South Morgan Street, Chicago, IL 60607 Phone: 312-996-5847, Fax: 312-996-6465 Email: [email protected]
Additional ApplicationsActivity monitoring, analysis, recognition; video security, surveillance, retrieval, search, classification, recognition and mining.
Figure 1. Multi-target tracking from two cameras.
Figure 2. Graph decomposition: (a) 4-node directed acyclic graph and (b) partition of the graph into 3 sets by an antichain decomposition.
© 2010 Board of Trustees of the University of Illinois
Project Title: Multi-Object Trajectory-Based Activity AnalysisInvestigator(s): Dan Schonfeld and Ashfaq Khokhar
Problem StatementThe aim of this project is to provide a flexible multi-object trajectory-based activity analysis framework for video retrieval and classification that integrates visual information from multiple cameras and indexed based on high-order tensor decomposition. The goal is to provide an efficient method for video retrieval and classification while using a flexible indexing structure that can accommodate an arbitrary number of objects in query/database.
Technical Approach
Key Achievements•Previous work on multi-object trajectory analysis ;•Previous work on multi-camera trajectory analysis ;•Previous work on tensor-based trajectory representation;•Previous work on high-order tensor decomposition ;•Previous work on trajectory analysis supported by NSF;•Incorporated in online demo.
ROM and ScheduleMulti-object, multi-camera trajectory analysis C/C++ codeEstimated Cost: $150K
Products and Deliverables•Multi-object, multi-camera trajectory retrieval and classification;•High-order tensor decomposition;•Flexible framework for arbitrary number of objects in query/database.
Contact Info: Dan Schonfeld, Univ. Illinois at Chicago, 851 South Morgan Street, Chicago, IL 60607 Phone: 312-996-5847, Fax: 312-996-6465 Email: [email protected]
Additional ApplicationsActivity monitoring, analysis, recognition; video security, surveillance, retrieval, search, classification, recognition and mining.
Figure 1. Video event detection and retrieval from two motion trajectories in the CAVIAR dataset: (a) query; (b) most-similar match; (c) second-most-similar match; (d) most-dissimilar match; (e) second-most-dissimilar match.
Figure 2. Tensor-space representation of multiple-object trajectories.
Project Title: Robust Video StabilizationInvestigator(s): Dan Schonfeld and Magdi Mohamed (Motorola)
For a demo, please see:http://www.youtube.com/watch?v=aUUYppaJPnw
Research Areas
• Image and Video Processing• Image and Video Retrieval• Image and Video Networks• 3D Imaging & Plenoptics• Computer Vision• Object Tracking• Object Recognition• Nonlinear Filtering• Sensor Networks• Machine Learning