# Correcting Ski Resort Trajectories Extracted from Video
## 3 Discussion  
While PeTrack has proven effective for extracting skier trajectories, its performance is influenced by certain environmental and video-specific factors: 1. Motion blur: Rapid skier movements occasionally caused motion blur, leading to minor inaccuracies in trajectory tracking. 2. Low resolution or contrast: Frames with poor lighting or distant skiers posed challenges for consistent detection and tracking. 3. Camera perspective variations: Changes in camera perspective impacted the consistency of trajectory extraction, particularly at the edges of the field of view. These limitations may slightly impact the precision of the extracted trajectories but do not undermine the overall trends and conclusions drawn from the data. Although PeTrack was sufficient for the scope of this study, future work will involve a comparison with other state-of-the-art trajectory extraction tools, such as DeepLabCut or OpenPose, to assess their suitability for dynamic skiing environments.  
Our study provides a novel methodology for correcting skier trajectories extracted from video data, offering a foundational tool for safety assessments in ski resorts. The methodology is specifically designed for cases where much information needed to properly correct the extracted data for effects like perspective and lens distortion is not available. This is often the case for webcame footage or private videos, which otherwise provide interesting insights into the dynamics of downhill skiing. The practical implications of this work are noteworthy. Corrected trajectory data allow ski resort operators to identify high-risk zones, optimize trail designs, and implement effective safety protocols. Furthermore, safety engineers can utilize the refined data to design better collision-prevention systems, such as predictive monitoring tools. Policymakers, on the other hand, can leverage these insights to establish evidence-based safety regulations, ensuring safer skiing environments for diverse populations.  
Although this study focuses on video-based trajectory correction methods, we recognize the potential of integrating digital terrain models (DEM) or digital surface models (DSM) to improve ski slope modeling accuracy. DEM/DSM data can provide detailed information about slope gradients, surface irregularities, and terrain features, which are crucial for enhancing trajectory corrections. For instance, combining DEM/DSM data with video-derived trajectories could allow for a more realistic representation of skier movements, especially in areas with complex terrain. However, the integration of DEM/DSM data presents certain challenges. Co-registering trajectory data with terrain surfaces requires accurate alignment of video coordinates and DEM/DSM spatial data, which involves additional computational steps and precise calibration. Furthermore, obtaining high-resolution DEM/DSM data may be subject to limitations in data availability and cost. Despite these challenges, the potential benefits of such integration are substantial and warrant further exploration.  
Real-world ski slopes often exhibit non-uniform geometries, including varying widths and gradients. These irregularities introduce challenges when applying manually selected reference lines for perspective correction. To overcome these limitations, automated approaches, such as homography transformations, can be employed. Homography transformations use multiple reference points to map distorted video planes to real-world planes, enabling dynamic correction of perspective effects. This technique has been widely used in computer vision and could be integrated into future versions of our methodology. Additionally, incorporating calibration markers or grids on the ski slope could provide a systematic means of defining reference points and improving the accuracy and consistency of the corrections. These grids, combined with homography transformations, would allow for automatic scaling and adjustment based on the slope's unique geometry.