This presentation summarizes a vision-based fall detection and alert system for elderly and disabled people. The system uses video analysis to detect falls. It performs motion detection, tracking, feature extraction and fall analysis. Motion is detected using background subtraction and morphological operations. Objects are tracked using mean-shift tracking and multiple region tracking. Features like width, height, angle and speed are extracted and filtered. Falls are detected using rules based on these features. The system was tested on 15 fall datasets with 80% detection rate and few false positives. Future work to improve motion tracking and use supervised learning for decision making is discussed.

1. Prince of Songkhla University Department of Computer Engineering
24th JCAART’09 Conference
Vision-based Fall Detection and
Alert System Suitable for the Elderly
and Disabled Peoples
Teerasak Kroputaponchai and Dr. Nikom Suvonvorn
Presented by Assoc. Prof. Dr. Pornchai Phukpattaranont
Faculty of Engineering
Prince of Songkla University, Thailand
Teerasak Kroputaponchai and Nikom SUVONVORN August 26-28, 24th JCAART 2009 Conference

2. Prince of Songkhla University Department of Computer Engineering
Outline
• Problem statement
• System Overview
• Motion detection and tracking
• Features extraction
• Fall analysis and detection
• Result and discussion
• Conclusion and future work
Teerasak Kroputaponchai and Nikom SUVONVORN August 26-28, 24th JCAART 2009 Conference

3. Prince of Songkhla University Department of Computer Engineering
Problem statement
• Falls event amongst the elderly are particularly
serious and often lead to injury or death
• Automatic monitoring of the activities of daily living
(ADL) and falls event for the elderly and disabled
people using image sequences analysis leads to the
immediate or preventive intervention.
Teerasak Kroputaponchai and Nikom SUVONVORN August 26-28, 24th JCAART 2009 Conference

4. Prince of Songkhla University Department of Computer Engineering
System overview
Motion detection
Features extraction
Fall detection
Motion Tracking
Teerasak Kroputaponchai and Nikom SUVONVORN August 26-28, 24th JCAART 2009 Conference

5. Prince of Songkhla University Department of Computer Engineering
Motion detection
• Use background subtraction technique
– Running Average with selectivity
⎧αFi ( x, y ) + (1 − α ) Bi ( x, y ) if Fi ( x, y ) is background
Bi +1 ( x, y ) = ⎨
⎩ Bi ( x, y ) else
⎧255 if Bi ( x, y ) − Fi ( x, y ) < τ
Oi +1 ( x, y ) = ⎨
⎩ 0 else
• Morphological operation
– Opening and Closing
– Noise suppression
Teerasak Kroputaponchai and Nikom SUVONVORN August 26-28, 24th JCAART 2009 Conference

6. Prince of Songkhla University Department of Computer Engineering
Motion segmentation
• Spatial-based region-fusion operation
– Two regions are the same object if they are overlapped or
their distance less than a specific threshold
– Very sensible to light condition : shadow, contrast
changing and sudden changes of light
Teerasak Kroputaponchai and Nikom SUVONVORN August 26-28, 24th JCAART 2009 Conference

7. Prince of Songkhla University Department of Computer Engineering
Motion segmentation on tracking
• Texture-based region-fusion during tracking process
– the color probability density of object’s texture is
additionally applied as a similarity measurement between
regions
– Mixture motion-texture model can reduce noises and
increases significantly the effectiveness of algorithm
(a) Image sequence (b) motion detection (c) texture detection (d) mixture of motion-texture
Teerasak Kroputaponchai and Nikom SUVONVORN August 26-28, 24th JCAART 2009 Conference

8. Prince of Songkhla University Department of Computer Engineering
Motion tracking by Mean-shift
• Mean-shift tracking
– Iterative procedure that shifts pixel’s intensity to the
average of its neighborhood on the color probability
density.
• Multiple-regions tracking
– Upper body and Lower body parts
Teerasak Kroputaponchai and Nikom SUVONVORN August 26-28, 24th JCAART 2009 Conference

9. Prince of Songkhla University Department of Computer Engineering
Features extraction
• Object (Human?) characteristics
– Width and Height of object region
– Angle between y-axis and horizontal line
• Object principle axis is calculated from region moment
– Speed of the extremity points of y-axis
• Computed as the ratio of moving distance between frames and
time
θ H
W
Teerasak Kroputaponchai and Nikom SUVONVORN August 26-28, 24th JCAART 2009 Conference

10. Prince of Songkhla University Department of Computer Engineering
Features extraction
• Noises suppression
– Noisy features obtained from non-perfect tracking process
• Butterworth low-pass filter
– To consider the fall characteristic factors : the frequency
cut must higher than the fall frequencies (0.4s-0.8s)
– Kernel parameters is 1/22 [1/6, 1/2, 1/1.06, 1, 1/1.06, 1/2, 1/6]
– Apply to the five features…
Teerasak Kroputaponchai and Nikom SUVONVORN August 26-28, 24th JCAART 2009 Conference

11. Prince of Songkhla University Department of Computer Engineering
Features to fall analysis
(a) Fall (b)
Fall Lie
Lie
Fall (c)
Lie
(a) Angle
(b) Width and Height
(c) Speed of extremity points
Teerasak Kroputaponchai and Nikom SUVONVORN August 26-28, 24th JCAART 2009 Conference

12. Prince of Songkhla University Department of Computer Engineering
Fall detection
• Expert and Rules based decision
Fall detection
Yes
Yes
Yes
Lie detection
H = Height W = Width
t = Time V = Speed
delta = Fall angle
Teerasak Kroputaponchai and Nikom SUVONVORN August 26-28, 24th JCAART 2009 Conference

13. Prince of Songkhla University Department of Computer Engineering
Result and discussion
• Dataset
– 15 Fall datasets was done in a indoor situation using
volunteers
• Result
T-Shirt Pants Image Fall False
Color Color Sequences Detection positives
Orange Blue 5 80% 1
White Blue 5 60% 2
Green White 5 80% 1
• Most false positives is caused by non-perfect motion detection
and tracking process.
Teerasak Kroputaponchai and Nikom SUVONVORN August 26-28, 24th JCAART 2009 Conference

14. Prince of Songkhla University Department of Computer Engineering
Result and discussion
• Demo
Teerasak Kroputaponchai and Nikom SUVONVORN August 26-28, 24th JCAART 2009 Conference

15. Prince of Songkhla University Department of Computer Engineering
Result and discussion
• Error
Teerasak Kroputaponchai and Nikom SUVONVORN August 26-28, 24th JCAART 2009 Conference

16. Prince of Songkhla University Department of Computer Engineering
Future work
• Improve the motion and tracking method
• Improve human
modeling
• Decision method
– Decision rules by the expert
to by supervised learning
Teerasak Kroputaponchai and Nikom SUVONVORN August 26-28, 24th JCAART 2009 Conference

17. Prince of Songkhla University Department of Computer Engineering
References
• G. Perolle, P. Fraisse, M. Mavros and I. Etxeberria. , “Automatic Fall Detection and Activity
Monitoring for Elderly,” In Proceedings of MEDETEL, 2006.
• Chia-Wen Lin and Zhi-Hong Ling., “Automatic Fall Incident Detection in Compressed Video for
Intelligent Homecare,” Computer Communications and Networks 2007, pp.1172 – 1177,2007.
• R. C. Gonzalez and R. E. Woods, Digital Image Processing, Prentice Hall, (2nd Edition) 2002.
• W. Hu, T. Tan, L. Wang, and S. Maybank, “A survey on visual surveillance of object motion
and behavior,” IEEE trans. Systems, Man, and Cybernetics- Part C: Applications and Reviews,
vol. 38, no. 3, pp.334-352, Aug. 2004.
• R. Cucchiara, A. Prati and R. Vezzani “A Multi-Camera Vision system for Fall Detection and
Alarm Generation,” Expert Systems Journal , vol. 5 , Blackwell Publishing. 2007.
• J. K. Aggarwai, Q. Cai, “Human Motion Analysis: A review,” Computer Vision and Image
Understanding, Vol. 73, pp.428-440,1999
• M. Piccardi, “Background subtraction techniques: a review”, in Proc. of IEEE SMC 2004
International Conference on Systems, Man and Cybernetics, The Hague, The Netherlands,
October 2004.
Thanks you
Teerasak Kroputaponchai and Nikom SUVONVORN August 26-28, 24th JCAART 2009 Conference