Self-Evaluation for Video Tracking Systems

Abstract

In this paper, we present an algorithm for automatic performance evaluation of a video tracking system that does not require ground-truth data. Such an algorithm can play an important role in automatically determining when the underlying system loses track and needs re-initialization. The algorithm is based on measuring appearance similarity and tracking uncertainty. Several experimental results on vehicle and human tracking are presented. Effectiveness of the evaluation scheme is assessed by comparisons with ground truth. The proposed self evaluation algorithm has been used in an acoustic/video based moving vehicle detection and tracking system.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 2004
Accession Number
ADA431637

Entities

People

  • Hao Wu
  • Qinfen Zheng

Organizations

  • University of Maryland

Tags

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Aspect Ratio
  • Automatic
  • Cameras
  • Computer Vision
  • Detection
  • Engineering
  • Image Processing
  • Indicators
  • Sequential Monte Carlo Methods
  • Surveillance
  • Test And Evaluation
  • Trajectories
  • Uncertainty
  • Universities
  • Video Surveillance

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computer Vision.
  • Systems Analysis and Design