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

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goals and plan for year 1 and out years richard baraniuk rice
Goals and Planfor Year 1 and Out YearsRichard Baraniuk (Rice)

ARO MURI | Opportunistic Sensing | Rice, Maryland, Illinois, Yale, Duke, UCLA | October 2009

research thrusts
Research Thrusts

ARO MURI | Opportunistic Sensing | Rice, Maryland, Illinois, Yale, Duke, UCLA | October 2009

thrust 1 project goals
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

thrust 1 year 1 goals
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

thrust 2 project goals
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
thrust 2 project goals1
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
thrust 2 year 1 goals
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
thrust 3 project goals
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

thrust 3 year 1 goals
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

thrust 4 goals
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
government caucus and debriefing duncan hall 1044
Government Caucusand DebriefingDuncan Hall 1044

ARO MURI | Opportunistic Sensing | Rice, Maryland, Illinois, Yale, Duke, UCLA | October 2009