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.
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 language | English |
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Title of host publication | Proceedings of the International Conference on Computer Vision (ICCV 2017) |
Publisher | IEEE Computer Society Press |
Number of pages | 9 |
ISBN (Electronic) | 9781538610329 |
ISBN (Print) | 9781538610336 |
DOIs | |
Publication status | Published - 25 Dec 2017 |
Event | International Conference on Computer Vision (ICCV 2017) - Venice, Italy Duration: 22 Oct 2017 → 29 Oct 2017 |
Publication series
Name | IEEE International Conference on Computer Vision (ICCV) |
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ISSN (Electronic) | 2380-7504 |
Conference
Conference | International Conference on Computer Vision (ICCV 2017) |
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Country/Territory | Italy |
City | Venice |
Period | 22/10/17 → 29/10/17 |