Crowd Analysis at Mass Transit Sites

Prahlad Kilambi, Osama Masound, and Nikolaos

Papanikolopoulos

University of Minnesota

Proceedings of IEEE ITSC 20062006 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 4 3.0 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