Vlad I. Morariu  

 

 

 

I am a PhD student at University of Maryland, College Park. My advisor is Dr. Larry Davis, and I am associated with the Computer Vision Lab and the Institute for Advanced Computer Studies. I completed my B.S. and M.S. degrees at Penn State University (with Dr. Octavia Camps as my research advisor). My research interests are in Computer Vision, Pattern Recognition, Machine Learning, and Artificial Intelligence.

 

Research Projects:

  • Video Activity and Situation Analysis
    I am currently working on activity and situation analysis of scenarios such as parking lot robberies. This work entails low level analysis, such as tracking humans and recognizing subtle gestures, as well as high level reasoning that incorporates knowledge about the world in reasoning about scenarios.

  • Representing Visibility Context for Action Understanding
    We proposed a representation of visibility/spatial context based on visibility features (obtained from isovists and visibility graphs) that is suitable for human action understanding. Using a Bayes net, we then used our visibility context representation to reason about 2-dimensional trajectories (top view) generated by an agent performing a simple search-based task in various layouts. Human subjects were asked to interpret the trajectories 1) to demonstrate that knowledge of visibility context improves interpretation of our task and 2) to provide a baseline against which our algorithm can be compared. Our framework was able to match the performance of humans.

  • Appearance Modeling for Multi-camera Correspondence and Tracking
    (M.S. research)
    We learned generative appearance models by extracting object appearance from single or multiple views and learning its evolution on a manifold over time. Using target dynamics, we were able to predict future appearance in each view. In the multiple view case we learned correspondences between the implicit low-dimensional representation of each high-dimensional object view by either aligning low-dimensional coordinates during nonlinear manifold learning, or learning the dynamics of how low-dimensional coordinates in separate views evolved together over time. Our model allowed us to "hallucinate" the appearance of occluded targets by 1) predicting future appearance in each view temporally or by 2) predicting the appearance in one view given the appearance in another.

Publications:

  • Vlad I. Morariu, V. Shiv Naga Prasad, and Larry S. Davis. Human Activity Understanding using Visibility Context. IEEE/RSJ IROS Workshop: From sensors to human spatial concepts (FS2HSC), 2007. PDF BibTeX

  • Benjamin Fransen, Vlad Morariu, Eric Martinson, Samuel Blisard, Matthew Marge, Scott Thomas, Alan Schultz, and Dennis Perzanowski. Using Vision, Acoustics, and Natural Language for Disambiguation. IEEE International Conference on Human-Robot Interaction (HRI) 2007. PDF BibTeX

  • Vlad I. Morariu, Octavia I. Camps, Mario Sznaier, and Hwasup Lim. Robust Cooperative Visual Tracking: A Combined Nonlinear Dimensionality Reduction/Robust Identification Approach. In Advances in Cooperative Control and Optimization, M. Hirsch, R. Murphey, P. Pardalos and D. Grundel, Eds., Springer Verlag, 2007. PDF BibTeX

  • Vlad I. Morariu and Octavia I. Camps. Modeling Correspondences in Multi-camera Tracking using Nonlinear Manifold Learning and Target Dynamics. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2006. PDF BibTeX

  • Hwasup Lim, Vlad I. Morariu, Octavia I. Camps, and Mario Sznaier. Dynamic Appearance Modeling for Human Tracking. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2006. PDF BibTeX

Software:

  • FIGTree - Fast Improved Gauss Transform with Tree Data Structure (FIGTree Homepage)
    A library for fast computation of Gauss transforms in multiple dimensions, using the Improved Fast Gauss Transform and Approximate Nearest Neighbor searching. The nearest neighbor searching is performed using the ANN library, available at http://www.cs.umd.edu/~mount/ANN/. This software allows for efficient computation of probabilities by Kernel Density Estimation (KDE), and can reduce complexity of algorithms commonly used in Computer Vision, Machine Learning, etc, that must evaluate the Gauss transform. The publications related to this approach are provided on Vikas Raykar's page. NOTE: currently, Vikas also provides the previous version of the IFGT code (MATLAB interface only), which did not use a tree data structure. The FIGTree library includes the IFGT code from that page with fixes and additional features, and also provides a C/C++ interface.