Multiscale Architectures and Parallel Algorithms for Video Object Tracking

Abstract

Implementation and performance of an extended set of parallel multicore video processing chain of modules were investigated including Cell/B.E. flux tensor for motion detection, morphology, connected component labeling, and object statistics for blob extraction. A limitation of earlier work included unsatisfactory end-to-end performance and a need for better integration within the Net-Centric Exploitation and Tracking (N-CET) software framework. We examined software integration for Phoenix support, reducing thread overhead, video processing module interoperability, enhancing streaming performance, evaluating power requirement tradeoffs between different algorithms, assisting with tighter integration of the individual modules and gathering benchmarking data to analyze performance bottlenecks in communication pathways and computational algorithms. The video processing modules were ported to the IBM QS20/QS22 Blade architectures with 16 Synergistic Processing Elements for improved numerical computation performance, especially for the flux tensor computation.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 2011
Accession Number
ADA550808

Entities

People

  • Kannappan Palaniappan

Organizations

  • University of Missouri

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Algorithms
  • Central Processing Units
  • Computational Complexity
  • Computer Programming
  • Computer Programs
  • Computer Vision
  • Computers
  • Detection
  • Image Processing
  • Information Science
  • Operating Systems
  • Parallel Computing
  • Parallel Processing
  • Sensor Networks
  • Statistics

Fields of Study

  • Computer science

Readers

  • Distributed Systems and Data Platform Development
  • Parallel and Distributed Computing.
  • Sensor Fusion and Tracking Systems.