Event Representation in Humans and Machines

Abstract

One of the most compute intensive tasks in analyzing naturalistic video is tracking objects and people. Tracking complete databases containing hundreds of thousands of hours of video has traditionally been extremely time consuming and/or expensive. The massively parallel, low arithmetic precision, SIMD architecture proposed by Bates was studied to determine whether it could bring great efficiency benefits to tracking. The slowest subtasks in the tracking pipeline were studied, and it appears that tracking is a task that maps well to the proposed hardware, with the potential for thousands of times speedup, lower energy use, and cost, compared to traditional CPU-based methods.

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Document Details

Document Type
Technical Report
Publication Date
Sep 30, 2011
Accession Number
ADA550111

Entities

People

  • Deb Roy
  • Joseph Bates

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Air Platforms
  • Autonomy
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Arithmetic
  • Brightness
  • Coders
  • Coding
  • Compression
  • Compression Ratio
  • Convolution
  • Databases
  • Decoding
  • Decompression
  • Digital Computers
  • Energy Consumption
  • Floating Point Operations
  • Frequency
  • Pipelines
  • Precision

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Computer Vision.
  • Parallel and Distributed Computing.