3-D Tracking Research: Learning Correspondence from Static 3D Points Causes 3D Object Tracking to Emerge

Abstract

Problem: Aerial surveillance demands full attention to video by PED teams Manual, error-prone process Technical barriers including object detection, and tracking Limitations result in poor pattern detection in a surveilled region Vehicle tracks used to train LSTM autoencoder that learns normal behavior in order to identify anomalous tracks Results shown are for perfect data -- reality is not so pretty due to inadequate object detection and tracking This results in lost tracks and many track lets that are difficult to correlate Solution Work directly with DoD to improve pattern detection in aerial surveillance data patterns Work with researchers to address core technology problems of tracking of objects Impact (FY1820) Improved DoD pattern detection in aerial surveillance data Developing unsupervised 3D tracking algorithms to improve on other unsupervised methods and achieve performance similar to supervised methods

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2020
Accession Number
AD1110827

Entities

Organizations

  • Carnegie Mellon University

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Automated Text Summarization
  • Contracts
  • Copyrights
  • Department Of Defense
  • Detection
  • Engineering
  • Governments
  • Guarantees
  • Intellectual Property
  • Law
  • Materials
  • Patents
  • Software Development
  • Surveillance
  • Tracks
  • Trademarks
  • Universities
  • Vehicle Tracks
  • Vehicles

Fields of Study

  • Computer science

Readers

  • Computer Vision.
  • Neural Network Machine Learning.
  • Sensor Fusion and Tracking Systems.