Gait Analysis for Human Identification (GAHI)

Supervisors Prof.Dr. Ahmed Mohammad Hamad. Dr. Eman Shaaban. TA. Mohammad Hamdy.

Team Members Agwad Hammad El-Sayed. (Csys) Sherif Saad El-Din Hussein. (IS) Salah Eldin Emam Salah. (IS) Saed Ezz Elarab Abd Elhameed.(IS)

Agenda Problem Definition Objective Development Phases Project Demo Conclusion Tools References

Problem Definition Gait definition Human identification approaches Gait – “A person’s manner of walking” Human identification approaches Face Fingerprint Gait

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 ?!

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.

Development Phases Human Detection and Tracking Feature Extraction Training or Classification

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

Foreground Detection Segment moving objects form background Gaussian Temporal Subtraction Wide rage of environments Indoor & outdoor Adaptive on changes happened in environments

Foreground Detection Segmentation Process Initial Back ground Image Current Image Segmented Image

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

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

Object Segmentation Aims to extract connected object from image

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

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

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

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.

Object Classification Algo. Jack Hoang Algorithm Based on Codebook theory. Feature Vector for the Object Training Classifying or

Object Classification Algo. Creating Feature Vector We normalize the size of object to 20 by 40. Normalized 40 20

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 &

Object Classification Algo. The Feature Vector is created by the set of these distances. So, the dimension of the shape vector is 20.

Object Classification Algo. Training Feature Vector Using distortion sensitive competitive learning (DSCL) To make equal distortion regions for training vectors

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.

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

Development Phases Human Detection and Tracking Feature Extraction Training or Classification

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

Size Normalization & Horizontal Alignment Silhouette images will be aligned to be in the image center and sized to be the same width and Height

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

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

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

Gait Energy Image(GEI) Silhouette images will be accumulated to generate Gait Energy Image(GEI). Silhouette Images GEI Image

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

Feature Selection Mask Human carrying a bag Generate Feature Selection Mask and Apply it to New GEI & Stored GEIs Human wearing a coat

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

How to Apply Mask? Gallery GEI Probe GEI AND Apply Apply

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

Development Phases Human Detection and Tracking Feature Extraction Training or Classification

Training or Classification Store extracted features Classification Recognize extracted features

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

Classification Direct template matching Statistical approaches

Statistical Approach Principal Components Analysis PCA Multiple Discriminant Analysis MDA

Principal Components Analysis PCA PCA is a powerful tool for analyzing data. PCA can reduce the number of dimensions without much loss of data.

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

Statistical Approach Principal Components Analysis PCA Multiple Discriminant Analysis MDA

Multiple Discriminant Analysis MDA PCA is followed by MDA aims to best separating data from different classes.

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

Project Demo DEMO DEMO

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.

Tools Software Hardware Visual Studio.NET. OpenCV Library. C# Language. OpenCV Library. Hardware Digital Camera.

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”. http://en.wikipedia.org/wiki/Connected_Component_Labeling.

Thanks 