Assessing tracking performance in complex scenarios using mean time between failures

Peter Carr, Robert T. Collins

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Existing measures for evaluating the performance of tracking algorithms are difficult to interpret, which makes it hard to identify the best approach for a particular situation. As we show, a dummy algorithm which does not actually track scores well under most existing measures. Although some measures characterize specific error sources quite well, combining them into a single aggregate measure for comparing approaches or tuning parameters is not straightforward. In this work we propose 'mean time between failures' as a viable summary of solution quality - especially when the goal is to follow objects for as long as possible. In addition to being sensitive to all tracking errors, the performance numbers are directly interpretable: how long can an algorithm operate before a mistake has likely occurred (the object is lost, its identity is confused, etc.)? We illustrate the merits of this measure by assessing solutions from different algorithms on a challenging dataset.

Original languageEnglish (US)
Title of host publication2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509006410
DOIs
StatePublished - May 23 2016
EventIEEE Winter Conference on Applications of Computer Vision, WACV 2016 - Lake Placid, United States
Duration: Mar 7 2016Mar 10 2016

Publication series

Name2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016

Other

OtherIEEE Winter Conference on Applications of Computer Vision, WACV 2016
Country/TerritoryUnited States
CityLake Placid
Period3/7/163/10/16

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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