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Video - based Fall Detection in Elderly's Houses. Høgskolen i Gjøvik 05.06.2008 Saleh Alaliyat. Outline. Introduction Background Proposed System Implementations Conclusion. Introduction. Objective and benefits:. - PowerPoint PPT Presentation
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Høgskolen i Gjøvik
05.06.2008
Saleh Alaliyat
Video - based Fall Detection in Elderly's Houses.
Video - based Fall Detection in Elderly's Houses.
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Outline Introduction Background Proposed System Implementations Conclusion
Video - based Fall Detection in Elderly's Houses.
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Introduction
Video - based Fall Detection in Elderly's Houses.
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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.
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Background
Video - based Fall Detection in Elderly's Houses.
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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
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Background 2
Indoor Surveillance
Segmentation and Tracking
Features extraction
Events Classification
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Proposed System
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Video Input Sequence
Background estimation
Segmentation (Foreground objects extraction)
Shadow Removal
Morphological operations: Dilatation.Erosion.Labeling
Tracking the objects
Matching, Merging and Splitting Module
Trac
king
Proc
ess
Fetures Extraction
Events Classification
Fall Detection
Alarm
Yes
Audio Signal
Audio Track Analysis No
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Implementations
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Segmentation
The aim is to have a foreground image that has only the moving objects.
Input (RGB) images
Background estimation
Segmentation (Foreground objects extraction)
Shadow Removal
Post-Processing (Morphological operations): Dilatation.Erosion.Labeling
Binary Image
Binary Improved
Foreground Binary Mask
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a: Background Reference
b: Current Frame
c: Absolute difference
d: Binary Image
e: shadow mask
f: Binary Improved
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Features extraction
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Applying median filter for all the extracted features for smothing.
Motion before using medianFilter.
Motion after using median (window = 13) for smothing.
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Aspect Ratio: using X-Y Projections method (projecting the foreground pixels onto x and y axises). Aspect Ratio = Height / Width.
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Orientation: The angle between the x-axis and the major axis of the ellipse that represent the blob
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Motion Quantity: Sum of the pixels that belong to the blob and moving.
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.
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Vertical direction of the center of mass.
MHI: Sum of the pixels values in the Motion History Image divided by the number of blob pixels.
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Audio:
Audia signal
Wavelet coefficients
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Sample window = 500;SNR: an indication of the difference in signal intesity.Test1: TV + talk + fallTest2: Music (song) + fallTest3: silence + Fall
Test1 Test2
Test3
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Events Classification
Audio Feature
Audio Track Analysis
Video Analysis
Features vector
MHI or Direction of Motion
> Threshod ?
Performs Majority Voting of K-NN algorithm
Aspect RatioHeight of CoMHeight of BBOrientationMajor axisMinor Axis
Pass Lying evidence Threshold
Motion Quantity & Speed > Threshod?
ALARM
Yes
Yes
Yes
No
No
No
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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 framesStart falling at frame #62Full falling (lying down at frame #77 Stay lying down for 21 frames. K-NN output is lying down for these 21 frames.
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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).
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Conclusion
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Conclusion: K-NN gives confident results. including the audio.
Future works: Define normal inactivity zones. Personal Information. 3D information.
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The end
Thank you
