  Surveillance footage represents a valuable resource and opportunities for
conducting gait analysis. However, the typical low quality and high noise
levels in such footage can severely impact the accuracy of pose estimation
algorithms, which are foundational for reliable gait analysis. Existing
literature suggests a direct correlation between the efficacy of pose
estimation and the subsequent gait analysis results. A common mitigation
strategy involves fine-tuning pose estimation models on noisy data to improve
robustness. However, this approach may degrade the downstream model's
performance on the original high-quality data, leading to a trade-off that is
undesirable in practice. We propose a processing pipeline that incorporates a
task-targeted artifact correction model specifically designed to pre-process
and enhance surveillance footage before pose estimation. Our artifact
correction model is optimized to work alongside a state-of-the-art pose
estimation network, HRNet, without requiring repeated fine-tuning of the pose
estimation model. Furthermore, we propose a simple and robust method for
obtaining low quality videos that are annotated with poses in an automatic
manner with the purpose of training the artifact correction model. We
systematically evaluate the performance of our artifact correction model
against a range of noisy surveillance data and demonstrate that our approach
not only achieves improved pose estimation on low-quality surveillance footage,
but also preserves the integrity of the pose estimation on high resolution
footage. Our experiments show a clear enhancement in gait analysis performance,
supporting the viability of the proposed method as a superior alternative to
direct fine-tuning strategies. Our contributions pave the way for more reliable
gait analysis using surveillance data in real-world applications, regardless of
data quality.
