Predicting Future Pedestrian Motion in Video Sequences using Crowd Simulation

TL;DR

This paper introduces a crowd simulation-based method to predict future pedestrian movements in videos, achieving high accuracy even in complex scenarios with an average error of 2.72cm for 32 pedestrians over 2 seconds.

Contribution

It presents a novel approach combining physics, heuristics, and BioCrowds for accurate future pedestrian motion prediction in real video sequences.

Findings

Average prediction error of 2.72cm for 32 pedestrians over 2 seconds

Effective in complex video scenarios with multiple events

Demonstrates potential for real-time pedestrian motion forecasting

Abstract

While human and group analysis have become an important area in last decades, some current and relevant applications involve to estimate future motion of pedestrians in real video sequences. This paper presents a method to provide motion estimation of real pedestrians in next seconds, using crowd simulation. Our method is based on Physics and heuristics and use BioCrowds as crowd simulation methodology to estimate future positions of people in video sequences. Results show that our method for estimation works well even for complex videos where events can happen. The maximum achieved average error is $2.72$cm when estimating the future motion of 32 pedestrians with more than 2 seconds in advance. This paper discusses this and other results.