Beyond standard benchmarks: Parameterizing performance evaluation in visual object tracking

Luka Cehovin Zajc, Alan Lukezic, Ales Leonardis, Matej Kristan

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

11 Citations (Scopus)
199 Downloads (Pure)

Abstract

Object-to-camera motion produces a variety of apparent motion patterns that significantly affect performance of short-term visual trackers. Despite being crucial for designing robust trackers, their influence is poorly explored in standard benchmarks due to weakly defined, biased and overlapping attribute annotations. In this paper we propose to go beyond pre-recorded benchmarks with post-hoc annotations by presenting an approach that utilizes omnidirectional videos to generate realistic, consistently annotated, short-term tracking scenarios with exactly parameterized motion patterns. We have created an evaluation system, constructed a fully annotated dataset of omnidirectional
videos and generators for typical motion patterns. We provide an in-depth analysis of major tracking paradigms which is complementary to the standard benchmarks and confirms the expressiveness of our evaluation approach.
Original languageEnglish
Title of host publicationProceedings of the International Conference on Computer Vision (ICCV 2017)
PublisherIEEE Computer Society Press
Number of pages9
ISBN (Electronic)9781538610329
ISBN (Print)9781538610336
DOIs
Publication statusPublished - 25 Dec 2017
EventInternational Conference on Computer Vision (ICCV 2017) - Venice, Italy
Duration: 22 Oct 201729 Oct 2017

Publication series

NameIEEE International Conference on Computer Vision (ICCV)
ISSN (Electronic)2380-7504

Conference

ConferenceInternational Conference on Computer Vision (ICCV 2017)
Country/TerritoryItaly
CityVenice
Period22/10/1729/10/17

Fingerprint

Dive into the research topics of 'Beyond standard benchmarks: Parameterizing performance evaluation in visual object tracking'. Together they form a unique fingerprint.

Cite this