Goals and Plan for Year 1 and Out Years Richard Baraniuk (Rice)

1. Goals and Planfor Year 1 and Out YearsRichard Baraniuk (Rice) ARO MURI | Opportunistic Sensing | Rice, Maryland, Illinois, Yale, Duke, UCLA | October 2009

2. Research Thrusts ARO MURI | Opportunistic Sensing | Rice, Maryland, Illinois, Yale, Duke, UCLA | October 2009

3. Thrust 1 Project Goals • Develop new data representations that integrate information from diverse sources • sparse and manifold representations for signal and image data • graphical sparse models • redundant frame representations • Towards an agnostic learning system that organizes information depending on the available opportunities and goals • Interplay between randomized and adaptive sensing • New sensor designs • Interaction with ATR and processing algorithms (Thrust 2) • tune representations to optimize ATR performance metrics • Interaction with navigation and optimization algs (Thrust 3) • tune representations to optimize platform performance metrics ARO MURI | Opportunistic Sensing | Rice, Maryland, Illinois, Yale, Duke, UCLA | October 2009

4. Thrust 1 Year 1 Goals • New theory • sparse graphical models • optimal scale for random encoding • frame-based classifiers • manifold-based data modeling for different target classes • Interactions • work with Thrust 2 team on practicalities of randomized encoding: background subtraction, normalization, equalization, etc. • work with Thrust 3 team to close loop on manifold-based navigation • work with Thrust 4 team to identify appropriate ARL/DOD data sets for theory and algorithm validation ARO MURI | Opportunistic Sensing | Rice, Maryland, Illinois, Yale, Duke, UCLA | October 2009

5. Thrust 2 Project Goals • Integrate manifold-based representation and statistical inference into ATR processing pipeline • develop randomized encoding-based background subtractionand tracking for air and ground videos • develop multisensor fusion theory and algorithms • Integrate multi-view tracking with OS to enable persistent tracking and recognition • feedback from track failures to control the sensors • based on online evaluation of trackers[such evaluations are available for particle filters, KLT, and mean shift trackers (PAMI to appear)] • Demonstrate effectiveness and supremacy of OS for object detection, object recognition, and activity recognition problems

6. Thrust 2 Project Goals • Develop scalable methods for learning and utilizing fine-grained contextual models to improve recognition • Develop low-dimensional representations for multi-class object recognition and identity maintenance (for tracking and tracklet correspondence) and for action recognition • Develop manifold-based methods for group activity representation and recognition, modeling and recognition of long duration activities • characterize model interactions among humans and static/dynamic objects in the scene • Develop language models for activity representation and recognition

7. Thrust 2 Year 1 Goals • Develop randomized encoding for background subtraction and tracking for air and ground videos. • Integrate multi-view tracking and opportunistic sensing to enable persistent tracking and recognition • feedback from track failures to control the sensors based on online evaluation of trackers • develop low dimensional representations for multi-class object recognition and identity maintenance (for tracking and tracklet correspondence) and for action recognition. • Incorporate FLIR models in sparsity-induced methods for ATR • Develop language models for activity representation and recognition • Investigate sparse and dense encoding of image patch distributions • Bayesian learning of leptokurtic distributions, integrated with ICA for de-noising and dereverberation

8. Thrust 3 Project Goals • Theory of optimal resource allocation for OS in dynamic and adversarial environments • Solution of the OS classification problem with multiple mobile sensor platforms • cooperative known environment • adversarial environment • Mission-driven coordination policies for multiple mobile sensor platforms in the presence of communication noise and jamming by intelligent adversaries ARO MURI | Opportunistic Sensing | Rice, Maryland, Illinois, Yale, Duke, UCLA | October 2009

9. Thrust 3 Year 1 Goals • Solution of the OS classification problem with one mobile sensing platform (Thrust 1 and Thrust 2) • Preliminary investigation of the OS classification problem with multiple robots • cooperative known environment • adversarial environment • Robustly optimal policies for multiple vehicles in the presence of communication jamming • Introduce diminishing returns in game theoretic framework ARO MURI | Opportunistic Sensing | Rice, Maryland, Illinois, Yale, Duke, UCLA | October 2009

10. Thrust 4 Goals • Project goals • validate theory and algorithms using data from Army/DOD labs • transition tools to Army/DOD labs • transition technology via student/postdoc interns • Year 1 goals • identify most appropriate ARL data for validation • preliminary validation of theory and algorithms

11. Government Caucusand DebriefingDuncan Hall 1044 ARO MURI | Opportunistic Sensing | Rice, Maryland, Illinois, Yale, Duke, UCLA | October 2009