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Gait Analysis for Human Identification (GAHI)
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Supervisors Prof.Dr. Ahmed Mohammad Hamad. Dr. Eman Shaaban.
TA. Mohammad Hamdy.
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Team Members Agwad Hammad El-Sayed. (Csys)
Sherif Saad El-Din Hussein (IS) Salah Eldin Emam Salah (IS) Saed Ezz Elarab Abd Elhameed.(IS)
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Agenda Problem Definition Objective Development Phases Project Demo
Conclusion Tools References
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Problem Definition Gait definition Human identification approaches
Gait – “A person’s manner of walking” Human identification approaches Face Fingerprint Gait
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Problem definition cont.
Gait analysis as identification method excellence Gait is attractive since it requires no subject contact, in common with automatic face recognition and other biometrics. How to recognize thieves when they wear masks and gloves ?!
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Objective Develop a system to recognize individuals derived from a video sequence of a person walking. The system should be able to: Extract the gait features to identify person. Store the derived gait signature for matching at recognition stage.
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Development Phases Human Detection and Tracking
Feature Extraction Training or Classification
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Human Detection & Tracking
Capture Video Background Image Background Model Current Image Foreground Detection Update Foreground images Object Segmentation Blobs Labeled Blobs Object Tracking Object Classification Human(s) Labels Gait Feature Extraction
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Foreground Detection Segment moving objects form background
Gaussian Temporal Subtraction Wide rage of environments Indoor & outdoor Adaptive on changes happened in environments
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Foreground Detection Segmentation Process Initial Back ground Image
Current Image Segmented Image
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pixels of its neighbors
Foreground Detection Shadow Removal Pn<Bn color components normalized pixels of its neighbors intensity values of This is Shadow There isn’t Shadow
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Human Detection & Tracking
Capture Video Background Image Background Model Current Image Foreground Detection Update Foreground images Object Segmentation Blobs Object Tracking Object Classification Labeled Blobs Human(s) Labels Gait Feature Extraction
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Object Segmentation Aims to extract connected object from image
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Human Detection & Tracking
Capture Video Background Image Background Model Current Image Foreground Detection Update Foreground images Object Segmentation Blobs Object Tracking Object Classification Labeled Blobs Human(s) Labels Gait Feature Extraction
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Tracking The aim of object tracking is to establish a correspondence mapping between detected objects across consecutive frames The person should have the same label in each frame
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Human Detection & Tracking
Capture Video Background Image Background Model Current Image Foreground Detection Update Foreground images Object Segmentation Blobs Object Tracking Object Classification Labeled Blobs Human(s) Labels Gait Feature Extraction
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Object Classification
Moving regions detected in video may correspond to different objects in real-world such as vehicles, Humans, ..., etc. Aims to Differentiate between Humans and any other objects.
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Object Classification Algo.
Jack Hoang Algorithm Based on Codebook theory. Feature Vector for the Object Training Classifying or
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Object Classification Algo.
Creating Feature Vector We normalize the size of object to 20 by 40. Normalized 40 20
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Object Classification Algo.
Then selecting 10 points in left side of the boundary, and compute their distances to left side of bounding box And selecting 10 points in right side of boundary, and compute their distance to left side of bounding box. Boundary of human Left Side Right Side &
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Object Classification Algo.
The Feature Vector is created by the set of these distances. So, the dimension of the shape vector is 20.
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Object Classification Algo.
Training Feature Vector Using distortion sensitive competitive learning (DSCL) To make equal distortion regions for training vectors
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Object Classification Algo.
Classify Feature Vector Is to find a feature vector from training vectors with the minimum distortion to the feature vector of object. If the minimum distortion is less than a threshold, this object is human.
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Human Detection & Tracking
Capture Video Background Image Background Model Current Image Foreground Detection Update Foreground images Object Segmentation Blobs Object Tracking Object Classification Labeled Blobs Human(s) Labels Gait Feature Extraction
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Development Phases Human Detection and Tracking
Feature Extraction Training or Classification
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Feature Extraction Silhouette Sequence Normalized Silhouette Sequence
Human Detection Silhouette Sequence Size Normalization & Horizontal Alignment Detect Gait Cycles Normalized Silhouette Sequence Gait Cycles Generate Gait Energy Image Generate Feature Selection Mask GEI for each Gait Cycle Feature Mask for each GEI Training & Classification
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Size Normalization & Horizontal Alignment
Silhouette images will be aligned to be in the image center and sized to be the same width and Height
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Feature Extraction Silhouette Sequence Normalized Silhouette Sequence
Human Detection Silhouette Sequence Size Normalization & Horizontal Alignment Detect Gait Cycles Normalized Silhouette Sequence Gait Cycles Generate Gait Energy Image Generate Feature Selection Mask GEI for each Gait Cycle Feature Mask for each GEI Training & Classification
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Gait Cycles Detection How does gait cycle happen?
The gait cycle begins when one foot contacts the ground and ends when that foot contacts the ground again. Move down Move up Move down
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Feature Extraction Silhouette Sequence Normalized Silhouette Sequence
Human Detection Silhouette Sequence Size Normalization & Horizontal Alignment Detect Gait Cycles Normalized Silhouette Sequence Gait Cycles Generate Gait Energy Image Generate Feature Selection Mask GEI for each Gait Cycle Feature Mask for each GEI Training & Classification
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Gait Energy Image(GEI)
Silhouette images will be accumulated to generate Gait Energy Image(GEI). Silhouette Images GEI Image
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Feature Extraction Silhouette Sequence Normalized Silhouette Sequence
Human Detection Silhouette Sequence Size Normalization & Horizontal Alignment Detect Gait Cycles Normalized Silhouette Sequence Gait Cycles Generate Gait Energy Image Generate Feature Selection Mask GEI for each Gait Cycle Feature Mask for each GEI Training & Classification
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Feature Selection Mask
Human carrying a bag Generate Feature Selection Mask and Apply it to New GEI & Stored GEIs Human wearing a coat
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Feature Selection Mask cont.
We divide G(x,y) vertically into two parts GU(x,y) and GL(x,y) representing the upper two third and the lower one third of the GEI respectively. Then Feature Selection Mask can be generated as
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How to Apply Mask? Gallery GEI Probe GEI AND Apply Apply
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Feature Extraction Silhouette Sequence Normalized Silhouette Sequence
Human Detection Silhouette Sequence Size Normalization & Horizontal Alignment Detect Gait Cycles Normalized Silhouette Sequence Gait Cycles Generate Gait Energy Image Generate Feature Selection Mask GEI for each Gait Cycle Feature Mask for each GEI Training & Classification
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Development Phases Human Detection and Tracking
Feature Extraction Training or Classification
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Training or Classification
Store extracted features Classification Recognize extracted features
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Training Store the New GEI (i.e. extracted features) with person’s information (such as label, name ,… etc). Extracted Features Gallery Database Person’s Info Label : 1 Name : ahmed Dept. : CSys
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Classification Direct template matching Statistical approaches
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Statistical Approach Principal Components Analysis PCA
Multiple Discriminant Analysis MDA
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Principal Components Analysis PCA
PCA is a powerful tool for analyzing data. PCA can reduce the number of dimensions without much loss of data.
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PCA cont. PCA finds components that are useful for representing data
However no reason to assume that components are useful for discriminating between data in different classes
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Statistical Approach Principal Components Analysis PCA
Multiple Discriminant Analysis MDA
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Multiple Discriminant Analysis MDA
PCA is followed by MDA aims to best separating data from different classes.
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MDA cont. The main principle of MDA is to increase the distance between the different classes and decrease the distance within the same class. Class 2 Class 1
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Project Demo DEMO DEMO
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Conclusion We Developed GAHI System having this Features:
Detecting Moving Objects. Objects Tracking. Objects Classification. Extract Gait Features of Humans. Gait Features Training. Individuals Recognition Based on their Gait Features.
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Tools Software Hardware Visual Studio.NET. OpenCV Library.
C# Language. OpenCV Library. Hardware Digital Camera.
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References Robust Human Detection and Tracking System Using a Human-Model-Based Camera Calibration Motion Detection for Video Surveillance Jianpeng Zhou and Jack Hoang I3DVR International Inc 780 Birch mount Road, Unit 16, Scarborough, Ontario, Canada M1K 5H4 “Real Time Robust Human Detection and Tracking System” Khalid Bashir, Tao Xiang and Shaogang Gong. ”Feature Selection for Gait Recognition without Subject Cooperation”.
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Thanks
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