Using synthetic data for person tracking under adverse weather conditions

Kerim, Abdulrahman and Celikcan, Ufuk and Erdem, Erkut and Erdem, Aykut (2021) Using synthetic data for person tracking under adverse weather conditions. Image and Vision Computing, 111 (104187). ISSN 1872-8138

Documents
Details

[+][-]

Documents

5965:119609

[+][-]

5965:119609

Using synthetic data for person tracking under adverse weather conditions (20MB)
Creative Commons Attribution Non-commercial No Derivatives 4.0.

Details

Creators:

Kerim, Abdulrahman and Celikcan, Ufuk and Erdem, Erkut and Erdem, Aykut

Description/Abstract:

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.

Item Type:

Article

Date:

27 April 2021

Funders:

UBITAK-1001 Program (GrantNo. 217E029), GEBIP 2018 fellowship of Turkish Academy of Sciences, BAGEP 2021 Award of the Science Academy

Identification Number:

10.1016/j.imavis.2021.104187