We present a novel real-time algorithm to predict the path of pedestrians in cluttered environments. Our approach makes no assumption about pedestrian motion or crowd density, and is useful for short-term as well as long-term prediction. We interactively learn the characteristics of pedestrian motion and movement patterns from 2D trajectories using Bayesian inference. These include local movement patterns corresponding to the current and preferred velocities and global characteristics such as entry points and movement features. Our approach involves no precomputation and we demonstrate the real-time performance of our prediction algorithm on sparse and noisy trajectory data extracted from dense indoor and outdoor crowd videos. The combination of local and global movement patterns can improve the accuracy of long-term prediction by 12-18% over prior methods in high-density videos.
Fig: We demonstrate the improved accuracy of our pedestrian path prediction algorithm (GLMP) over prior real-time prediction algorithms (BRVO, Const Vel, Const Accel) and compare them with the ground truth. We observe upto 18% improvement in accuracy.
This project was funded by Intel, The Boeing Company and National Science Foundation
If you would like to use the dataset, please contact the authors (Aniket Bera, Sujeong Kim and Dinesh Manocha) at ab@cs.unc.edu To see more work on motion and crowd simulation models in our GAMMA group, visit - http://gamma.cs.unc.edu/research/crowds/
