Predicting Future Pedestrian Motion in Video Sequences using Crowd Simulation
This work addresses the need for accurate pedestrian motion prediction in video analysis, which is incremental as it applies an existing crowd simulation method to a new application.
The paper tackles the problem of predicting future pedestrian motion in video sequences by using a crowd simulation method based on Physics and heuristics, achieving a maximum average error of 2.72 cm for estimating motion over 2 seconds ahead for 32 pedestrians.
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.