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[P] Best approach for online crowd density prediction from noisy video counts? (no training data)

Reddit r/MachineLearning / 3/25/2026

💬 OpinionIdeas & Deep AnalysisTools & Practical Usage
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Key Points

  • The author needs real-time, CPU-friendly forecasting of crowd density 5–10 frames ahead per zone using stable-but-noisy head counts (±10%) from P2PNet, with an additional goal of estimating time-to-reach a critical threshold.
  • Their current method (EMA-smoothed Gaussian-weighted linear extrapolation) yields MAE around 20 over 55 frames and only ~49% direction accuracy, especially poor on reversal events.
  • They have no historical training data, so they are looking for approaches that work online without model training.
  • They ask what alternative filtering/smoothing techniques to try (e.g., Kalman filtering, double exponential smoothing, or other methods) to improve both magnitude and directional forecasts.
  • The discussion is framed as an applied ML/forecasting problem with constraints: noisy measurements, no training data, short-horizon prediction, and CPU/runtime limitations.

I have per-frame head counts from P2PNet running on crowd video clips. Counts are stable but noisy (±10%). I need to predict density 5-10 frames ahead per zone, and estimate time-to-critical-threshold.

Currently using EMA-smoothed Gaussian-weighted linear extrapolation. MAE ~20 on 55 frames. Direction accuracy 49% (basically coin flip on reversals).

No historical training data available. Must run online/real-time on CPU.

What would you try? Kalman filter? Double exponential smoothing? Something else?

submitted by /u/WitnessWonderful8270
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