Crowd Analysis at Mass Transit Sites Prahlad Kilambi, Osama Masound, and Nikolaos Papanikolopoulos University of Minnesota Proceedings of IEEE ITSC IEEE Intelligent Transportation Systems Conference

abstract Detecting and estimating the count of groups, dense, and tracking them. The system can estimate in real-time. No constraints on camera placement Groups are tracked using Kalman filtering techniques

Introduction Previous proposed methods can’t count number of people, and track the crowds in real-time If the number of individuals increases, the system degrades drastically. e.g. Counts of people is used for crowd control.

Related Work Crowd monitoring using image processing, A. David Color pixels and edge pixels based W4: A real time system for detecting and tracking people, I. Haritaoglu Tracking individuals based on shape models Automatic estimation of crowd density using texture, A. Marana Estimate crowd density using neural network Bayesian human segmentation in crowded situations, T. Zhao Using Bayesian model

Overview of the Algorithm There are two mode of tracking people Tracks individuals Kalman filter technique count the number of people in group Extended Kalman filter tracker Based on humans, in general, move together with fixed gap between them

Heuristic-Based Extremely simple and efficient solution

Shape-based Shape probabilities based method blob model

Tracking Two purposes Avoid a number of false alarms including those due to other moving objects in the scene Occlusion handling Kalman filter tracker is valid for both single pedestrian and groups There are 3 tracking steps for occlusion handling Individuals or groups (Individual are taller than wider) People or vehicle (velocity threshold) If classified as group, Group tracker is initialized.

Experiments Experiments’ Settings three difference scene 8 difference positions camera height was varied from 27 feet to 90 feet the tilt of camera was varied from 20 to 40 degrees most crowded scene is up to 40 people Test PC Pentium GHz

Normalized per frame errors of larger groups over their life time

Plot of actual counts over the lifetime of a group of 11 people

Plot of maximum and minimum bounds on counts over the life time of group 11 people

Conclusion The system can count crowds of people accurately in real-time There are occasional problems with current method This method is required other cues like color and texture