Høgskolen i Gjøvik 05.06.2008 Saleh Alaliyat

1. Video - based Fall Detection in Elderly's Houses. Høgskolen i Gjøvik 05.06.2008 Saleh Alaliyat

2. Outline • Introduction • Background • Proposed System • Implementations • Conclusion Video - based Fall Detection in Elderly's Houses.

3. Introduction Video - based Fall Detection in Elderly's Houses.

4. Objective and benefits: • The main goal of this project is to detect person falling event in elderly’s houses and give an alarm in real-time. • Ensure the safety of elderly people: • Fast growing population of seniors. • Shortage of employees taking care of seniors. • The majority of injury-related hospitalizations for seniors result from falls. Video - based Fall Detection in Elderly's Houses.

5. Background Video - based Fall Detection in Elderly's Houses.

6. Background 1 Fall Detection techniques: • Sensors • wearable sensors. • Infrared sensors (vertical velocity). Drawbacks: forget to wear them and not sufficient to discriminate a fall from sitting. • Video – based mehtods Video - based Fall Detection in Elderly's Houses.

7. Background 2 • Indoor Surveillance • Segmentation and Tracking • Features extraction • Events Classification Video - based Fall Detection in Elderly's Houses.

8. Proposed System Video - based Fall Detection in Elderly's Houses.

9. Video - based Fall Detection in Elderly's Houses.

10. Implementations Video - based Fall Detection in Elderly's Houses.

11. Segmentation • The aim is to have a foreground image that has only the moving objects. Video - based Fall Detection in Elderly's Houses.

12. a: Background Reference b: Current Frame c: Absolute difference d: Binary Image e: shadow mask f: Binary Improved Video - based Fall Detection in Elderly's Houses.

13. Features extraction Video - based Fall Detection in Elderly's Houses.

14. Applying median filter for all the extracted features for smothing. Motion after using median (window = 13) for smothing. Motion before using medianFilter. Video - based Fall Detection in Elderly's Houses.

15. Aspect Ratio: using X-Y Projections method (projecting the foreground pixels onto x and y axises). Aspect Ratio = Height / Width. Video - based Fall Detection in Elderly's Houses.

16. Orientation: The angle between the x-axis and the major axis of the ellipse that represent the blob Video - based Fall Detection in Elderly's Houses.

17. Speed: the distance between the CoMs of the blob in a sequence of frames and divide it by the time. Height of the CoM: the distance between the CoM of the person and the floor. Motion Quantity: Sum of the pixels that belong to the blob and moving. Video - based Fall Detection in Elderly's Houses.

18. MHI: Sum of the pixels values in the Motion History Image divided by the number of blob pixels. Vertical direction of the center of mass. Video - based Fall Detection in Elderly's Houses.

19. Audio: Audia signal Wavelet coefficients Video - based Fall Detection in Elderly's Houses.

20. Sample window = 500;SNR: an indication of the difference in signal intesity.Test1: TV + talk + fallTest2: Music (song) + fallTest3: silence + Fall Test1 Test2 Test3 Video - based Fall Detection in Elderly's Houses.

21. Events Classification Video - based Fall Detection in Elderly's Houses.

22. K-NN: the activities are classified in groups, walking and standing, sitting, and lying down. • 24 short training movies (corridor in A-building and room A128. • the movies have walking, standing, sitting, kneeing and falling (lying down). • Make from them a trainng set for K-NN classifier (672). • Test the K-NN by applying two test movies. • Test1: 207 frames • Start falling at frame #62 • Full falling (lying down at frame #77 • Stay lying down for 21 frames. • K-NN output is lying down for these 21 frames. Video - based Fall Detection in Elderly's Houses.

23. MHI : frame # 82; (after 3 frames from lying). • Direction of motion: frame #68 (after 6 frames from fall starting). K-NN output is sitting (start giving Lying down at fame #72 to frame #117). • Check the speed and motion quantity for next 45 frames if the object still in the lying position. • Speed ( frame #82 to #105 = 23 frames). Video - based Fall Detection in Elderly's Houses.

24. Conclusion Video - based Fall Detection in Elderly's Houses.

25. Conclusion: • K-NN gives confident results. • including the audio. • Future works: • Define normal inactivity zones. • Personal Information. • 3D information. Video - based Fall Detection in Elderly's Houses.

26. The end Thank you  Video - based Fall Detection in Elderly's Houses.