1. Dan Schonfeld Co-Director, Multimedia Communications Laboratory Professor, Departments of ECE, CS & Bioengineering University of Illinois at Chicago

2. © 2010 Board of Trustees of the University of Illinois Picture/Illustration Project Title: Distributed Multi-Target Tracking Investigator(s): Dan Schonfeld Problem Statement The 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 Schedule Multi-target, multi-camera tracking C/C++ code Estimated 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: dans@uic.edu Additional Applications Activity 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.

3. © 2010 Board of Trustees of the University of Illinois Project Title: Multi-Object Trajectory-Based Activity Analysis Investigator(s): Dan Schonfeld and Ashfaq Khokhar Problem Statement The 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 Schedule Multi-object, multi-camera trajectory analysis C/C++ code Estimated 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: dans@uic.edu Additional Applications Activity 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.

4. Project Title: Robust Video Stabilization Investigator(s): Dan Schonfeld and Magdi Mohamed (Motorola) For a demo, please see: http://www.youtube.com/watch?v=aUUYppaJPnw

5. 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