Rights statement: This is the author’s version of a work that was accepted for publication in Image and Vision Computing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Image and Vision Computing, 111, 2021 DOI: 10.1016/j.imavis.2021.104187
Accepted author manuscript, 19.1 MB, PDF document
Available under license: CC BY-NC-ND
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Using synthetic data for person tracking under adverse weather conditions
AU - Kerim, A.
AU - Celikcan, U.
AU - Erdem, E.
AU - Erdem, A.
N1 - This is the author’s version of a work that was accepted for publication in Image and Vision Computing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Image and Vision Computing, 111, 2021 DOI: 10.1016/j.imavis.2021.104187
PY - 2021/7/31
Y1 - 2021/7/31
N2 - Robust visual tracking plays a vital role in many areas such as autonomous cars, surveillance and robotics. Recent trackers were shown to achieve adequate results under normal tracking scenarios with clear weather conditions, standard camera setups and lighting conditions. Yet, the performance of these trackers, whether they are correlation filter-based or learning-based, degrade under adverse weather conditions. The lack of videos with such weather conditions, in the available visual object tracking datasets, is the prime issue behind the low performance of the learning-based tracking algorithms. In this work, we provide a new person tracking dataset of real-world sequences (PTAW172Real) captured under foggy, rainy and snowy weather conditions to assess the performance of the current trackers. We also introduce a novel person tracking dataset of synthetic sequences (PTAW217Synth) procedurally generated by our NOVA framework spanning the same weather conditions in varying severity to mitigate the problem of data scarcity. Our experimental results demonstrate that the performances of the state-of-the-art deep trackers under adverse weather conditions can be boosted when the available real training sequences are complemented with our synthetically generated dataset during training.
AB - Robust visual tracking plays a vital role in many areas such as autonomous cars, surveillance and robotics. Recent trackers were shown to achieve adequate results under normal tracking scenarios with clear weather conditions, standard camera setups and lighting conditions. Yet, the performance of these trackers, whether they are correlation filter-based or learning-based, degrade under adverse weather conditions. The lack of videos with such weather conditions, in the available visual object tracking datasets, is the prime issue behind the low performance of the learning-based tracking algorithms. In this work, we provide a new person tracking dataset of real-world sequences (PTAW172Real) captured under foggy, rainy and snowy weather conditions to assess the performance of the current trackers. We also introduce a novel person tracking dataset of synthetic sequences (PTAW217Synth) procedurally generated by our NOVA framework spanning the same weather conditions in varying severity to mitigate the problem of data scarcity. Our experimental results demonstrate that the performances of the state-of-the-art deep trackers under adverse weather conditions can be boosted when the available real training sequences are complemented with our synthetically generated dataset during training.
KW - Person tracking
KW - Procedural generation
KW - Rendering
KW - Synthetic data
KW - Meteorology
KW - Correlation filters
KW - Lighting conditions
KW - Real-world sequences
KW - State of the art
KW - Synthetic sequence
KW - Tracking algorithm
KW - Training sequences
KW - Visual object tracking
KW - Object tracking
U2 - 10.1016/j.imavis.2021.104187
DO - 10.1016/j.imavis.2021.104187
M3 - Journal article
VL - 111
JO - Image and Vision Computing
JF - Image and Vision Computing
SN - 0262-8856
M1 - 104187
ER -