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Jitter removal with moving average

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@awestphal1 ci sta già lavorando.

Dal 7/9/2026.

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Descrizione

pre-processing

Summary

Implement a moving-average filter to reduce positional jitter in pedestrian trajectories. The filter should smooth each pedestrian's x/y coordinates independently while preserving trajectory metadata and frame alignment.

Background / Context

Trajectory data from recordings and simulations can contain small frame-to-frame position fluctuations. These fluctuations can distort derived quantities such as speed and acceleration.

Providing a built-in smoothing utility lets users reduce this noise consistently before applying analysis methods, without manually manipulating the underlying DataFrame.

Technical Details

  • Add the filtering functionality in the preprocessing API.
  • Accept a configurable moving-average window size in frames.
  • Smooth X_COL and Y_COL independently for each ID_COL, ordered by FRAME_COL.
  • Preserve the original pedestrian IDs, frame numbers, and TrajectoryData frame rate.
  • Define and document boundary behavior for incomplete windows at the beginning and end of each trajectory.
  • Validate the window size and raise an appropriate PedPy custom exception for invalid input.
  • Export the public API from pedpy/__init__.py and add it to __all__ if applicable.
  • Add unit tests under tests/unit_tests/ for normal operation, multiple pedestrians, boundaries, and invalid window sizes.

Acceptance Criteria

  • Users can apply a moving-average filter with a specified positive window size to a TrajectoryData instance or the trajectory of a specific pedestrian(s) (by ID)
  • Coordinates are smoothed independently per pedestrian and never use samples from another pedestrian
  • Output retains all original IDs, frames, non-coordinate columns, and trajectory metadata.
  • Boundary behavior is analyzed and implemented & documented:
    • apply filter where possible, remove other data (trajectory may become shorter).
    • adaptive window size at border
    • take inspiration from scipy
    • If window size is larger than trajectory, keep the original trajectory and warn the user
  • Use an existing implementation, e.g. scipy
  • Invalid window sizes (<=0) raise a PedPy custom exception with a useful error message.
  • Unit tests cover single- and multi-pedestrian trajectories, boundary frames, and invalid input.
  • The new public API is documented.
    • Describe how the window size may be chosen and influence on the result
    • Describe border behavior.
    • Describe in which scenarios the moving average may be used to remove jitter from trajectories.
    • Describe pre-conditions, when the filter may be applied, e.g., windows size vs length of trajectory.

First draft of the potential API:

traj = load_trajectory(foo)


trajectory_smoothed = moving_average(traj=traj, window_size=5, border_behavior=..)
trajectory_smoothed = moving_average(traj, ped_ids=[5, 10], window_size=5, border_behavior=..)
Lingua principale
Pascal
Stelle
35
Fork
18
Merge medio
8g 12h
PR unite (30g)
2

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