Machine learning engineer seeks real-time crowd density forecasting solution with P2PNet video counts but no training data available

Developer has noisy head count data (±10% error) from P2PNet running on crowd video and needs to predict density 5-10 frames ahead for specific zones

Current approach uses EMA-smoothed Gaussian-weighted linear extrapolation, achieving ~20 MAE on 55 frames but only 49% direction accuracy on trend reversals

Critical constraint: must operate online/real-time on CPU with no historical training data available

Seeking alternative methods like Kalman filters or double exponential smoothing to improve prediction accuracy and reliability