Multimedia Systems and Communication Research Multimedia Systems and Communication Research Department of Electrical and Computer Engineering Multimedia Systems Lab University of Illinois at Chicago Chicago, Illinois, USA Ashfaq Khokhar

Major Related Research Thrusts Multimedia Representation, Analysis, Communication, and Manipulation (Ansari, Schonfled and Khokhar)  Content based Indexing and Retrieval  Classification of Spatio-Tempral Image and Video Events  Motion Tracking  Digital Right Management  Parallel Implementations on GPU and multicore processors Heterogeneous Sensor Networks (Ansari, Zefran, and Khokhar)  Approximate Spatio-Temporal Query Processing,  Information Fusion, and Triggers  Motion Control Algorithms  Cross Layer Power Efficient Routing Solutions 2

3 Multimedia Representation, Analysis, Communication, and Manipulation Event/object retrieval and classification from video databases is an extremely challenging problem.  Query and/or stored video data undergo transformation due to camera or object motion (e.g. affine mapping).  Query and/or stored video data contain partial information (e.g. due to video occlusions).

4 Our Work o Scalable content based indexing and retrieval system for video events, images, and audio clips. o Classification of motion events, facial expressions, gestures o Tracking of multiple moving objects o Localized Null Space o Kernel Particle Filters o Hierarchical Distributed Indexing Structures o Distributed Hidden Markov Models

Proposed Localized Null Space Zero elements N-3 N Traditional Null Space 5 Structure of Localized Null Space Illustration of the structure of the traditional Null Space and the proposed Localized Null Space. Zero elements 3 Non-Zero elements N-3 Zero elements K-3 Non-Zero elements for W1 K N-K-3 Non-Zero elements for W2 3 Zero elements N-K

6 Benefits of LNS  Can be viewed as consisting of multiple subspace, therefore can be dynamically split for retrieval of partial queries.  Can be used to merge multiple Null Spaces into an integrated Null Space.  Has the same complexity as the traditional null space.

7 Trajectory and part of the rotated trajectory with identical localized null space representations. LNS Example

8 Application of LNS in Face Recognition 24 different poses used for each face from the UMIST database.

9 Application of LNS in Face Recognition Visual illustration of classification accuracy based on Localized Null Space Invariants when the query image is missing vertical or horizontal sections.

Multi-foveation videos Pixel foveation DCT foveation

Cyclic Motion Tracking (Click to play) Full body, Background clutter Occlusion

Heterogeneous Sensor Networks Joint work with Northwestern Univ. 12

Proposed Solution Hierarchical Novel Scalable Abstractions Hybrid Structure Rank Order Filters for Value Field Abstraction Multi-resolution Binary Maps for Sensor Location Abstraction Sensor Networks: In-network Hybrid Query Processing Example Query: Retrieve all the prairie regions in DuPage county that are near river and have between 15% and 45% of salinity decline. Solution Requirements Less Communication Less Maintenance Cost Less Storage Less Query Latency More Accurate Results Existing distributed solutions are incapable of handling value and location queries with equal efficiency!

Our Solution: Novel Hierarchical Abstractions Small and fixed size update messages across the hierarchical structure Immediate exact response for extreme values (minimum and maximum) Low latency, error bounded responses for range queries.

Small and fixed size update messages across the hierarchical structure Fast response for coarse view queries Low latency, energy efficient responses for fine detailed queries

What Can be Done for Nokia Parallel implementation of complete image processing pipeline on GPUs and multi-core platforms Scalable solutions for recognition/classification, and content based indexing and retrieval of images, audio, and video events.  Solutions will work under affine transformations In network indexing and querying solutions for approximate query processing 16