Multiple object tracking for fall detection in real-time surveillance system

Authors:
Young-Sook Lee;HoonJae Lee
Affiliations:
Dept. of Ubiquitous IT, Graduate School of Design & IT, Dongseo University, Busan, Korea;Division of Computer & Information Engineering, Dongseo University, Busan, Korea
Venue:
ICACT'09 Proceedings of the 11th international conference on Advanced Communication Technology - Volume 3
Year:
2009

Citing 5
Cited 1

Learning Patterns of Activity Using Real-Time Tracking

IEEE Transactions on Pattern Analysis and Machine Intelligence
The Recognition of Human Movement Using Temporal Templates

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Smart Sensor to Detect the Falls of the Elderly

IEEE Pervasive Computing
Activity Summarisation and Fall Detection in a Supportive Home Environment

ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Camera Calibration from Video of a Walking Human

IEEE Transactions on Pattern Analysis and Machine Intelligence

Real-Time fall detection method based on hidden markov modelling

ICISP'12 Proceedings of the 5th international conference on Image and Signal Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we present multiple object tracking to detect falls using a low-cost single and uncalibrated camera in real-time environment. Until now, existing studies for fall detection only presented their methods for a single person tracking. Occlusion problem is one of the main challenges in health care surveillance systems. For occlusion handling, 2D modeling tracking to multiple object tracking is not enough. The algorithm using 3D spatio-temporal templates is applied to occlusion problems for detecting and tracking people accurately. We use 2D trajectory information obtained in order to distinguish fall activities from normal daily activities. The experimental results show robust multiple object tracking and a good detection rate of falls in real-time.