24th jcaart 2009 conference

Prince of Songkhla University Department of Computer Engineering

Teerasak Kroputaponchai and Nikom SUVONVORN August 26-28, 24th JCAART 2009 Conference

Vision-based Fall Detection and Alert System Suitable for the Elderly

and Disabled Peoples

Faculty of EngineeringPrince of Songkla University, Thailand

Teerasak Kroputaponchai and Dr. Nikom Suvonvorn

Presented by Assoc. Prof. Dr. Pornchai Phukpattaranont

24th JCAART’09 Conference



Outline

• Problem statement• System Overview• Motion detection and tracking• Features extraction• Fall analysis and detection• Result and discussion• Conclusion and future work



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.



System overviewMotion detection

Motion Tracking

Features extraction

Fall detection



Motion detection• Use background subtraction technique

– Running Average with selectivity

• Morphological operation– Opening and Closing– Noise suppression

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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



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



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



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

W

Hθ



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…



Features to fall analysis

(a) Angle(b) Width and Height(c) Speed of extremity points

Fall

Lie

Fall Lie

Fall

Lie

(a) (b)

(c)



• Expert and Rules based decision

Fall detection

Fall detection

Lie detection

H = Height W = Widtht = Time V = Speeddelta = Fall angle

Yes

Yes

Yes



Result and discussion• Dataset

– 15 Fall datasets was done in a indoor situation using volunteers

• Result

• Most false positives is caused by non-perfect motion detection and tracking process.

T-Shirt Color

Pants Color

ImageSequences

FallDetection

False positives

Orange Blue 5 80% 1

White Blue 5 60% 2

Green White 5 80% 1



Result and discussion• Demo



Result and discussion• Error



• Improve the motion and tracking method

• Improve human modeling

• Decision method– Decision rules by the expert

to by supervised learning

Future work



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 2004International Conference on Systems, Man and Cybernetics, The Hague, The Netherlands, October 2004.

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24th jcaart 2009 conference

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