We present a novel, real-time algorithm to extract the trajectory of each pedestrian in moderately dense crowd videos. In order to improve the tracking accuracy, we use a hybrid motion model that combines discrete and continuous flow models. The discrete model is based on microscopic agent formulation and is used for local navigation, interaction, and collision avoidance. The continuum model accounts for macroscopic behaviors, including crowd orientation and flow. We use our hybrid model with particle filters to compute the trajectories at interactive rates. We demonstrate its performance in moderately-dense crowd videos with tens of pedestrians and highlight the improved accuracy on different datasets.
Fig: The left image highlights the tracked trajectories based on discrete motion models. The image on the right demonstrates the use of a hybrid motion model, using the continuum method for a cluster of pedestrians as well as discrete motion models for individuals. These clusters are computed in realtime based on frame coherence and pedestrian flow. The hybrid motion model can improve the tracking accuracy in these dense scenarios by 7-12% over prior methods.
"REACH - Realtime Crowd tracking using a Hybrid motion model" [PDF] Aniket Bera, Dinesh Manocha
2015 IEEE International Conference on Robotics and Automation, Seattle (ICRA 2015) [To Appear]
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 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/
