International Islamic University Chittagong
Department of CSE
Name: Fahamida Binte Jahangir Setu
ID: C191236
Semester:7th
Session: Spring-2022
Course Code: CSE-4750
Course Title: Technical Writing and Presentation
Course Teacher: Md. Khaliluzzaman
TITLE: Speed, Cloth and Pose Invariant Gait Recognition-Based Person Identification.
APPLICATIONS:
Gait analysis is very important in terms of security and identification purpose. It can also be
used for medical puepose and robot humanoid robot system.
Security
Identification
Medical purpose
Humanoid Robot
Animation
MOTIVATION:
1)
2)
3)
4)
5)
Biometric system is hugely used in identification,security,investigation etc.
Has highest collectivity.
Once it is acquired 40 and accustomed, it is very less likely to change with time.
Its easy to check and has no effect of changes in environment.
In the gait cycle, hip, knee and ankle 24 moves are distinct way to produce gait.
Challenges:
1) Robust recognition under various variances.
2) Modeling motion information in gait sequences.
3) Unfair performance comparison due to protocol variances.
4) Biometrics security, and privacy prevention.
PROBLEMS:
There are so many factors affecting gate analysis .For example,if we consider age of a person,
walking depends on muscle strength, which gradually changes as the person grown up. The
variations in muscle strength affect gait. First trimester during pregnancy: It is believed that
during pregnancy, the gait pattern shows changes. This method works only if there is only one
moving object. Hence, it is not suited for crowded scene.
METHODOLOGY:
Gait is a popularly known visual identification technique in which both upper and lowerbody
act in unison.The gait cycle has two distinct phase-1)Stance & 2)swing. In stance phase, the gait
cycle has initial contact, loading response, mid-stance and terminal stance. In swing phase, the
gait cycle has preswing, initial swing, mid-swing and terminal swing.
GEI(gait energy image) – Walking is a repetitive motion which is alaways same upto the
frequency which include particular sets of frame. GEI converts them into a single frame.
HOG(histograms of gradiants) –It is a feature descriptor technique used in image processing,
mainly for object detection and used to extract swift information from GEI images.
Radon Transform and Zernike Moments - HOG descriptors although being scale and
translational invariant are not rotational invariant. So the help of radon transform is taken from
the tomography field which takes the projection of a 2D image along with direction and give it a
1D profile. A projection of a two-dimensional function f is a set of line integrals and radon
function computes the line integrals from multiple sources along parallel paths, or beams, in a
certain direction.
Classifier model – It is the final step of our method. Detailed description of the model is given
in this step.
ACCURACY:
Speed Invariance:
This model is able to identify a person despite of any speed the person is walking and has a
highest accuracy of 99%.
Cloth Invariance:
Model
Accuracy
SVM
1.00
ANN
1.00
XGBOOST
0.97
This model can identify a person despite of the cloths the person is walking and has a highest
accuracy above 90%.
Pose Invariance:
This model is able to identify a person with regard to different views the camera have been
placed at with highest accuracy at 0 and 180 degree and in crossview the model worked best at
when trained at 72 degree and validated at 90 degree.
The error rate of the process is 0.7%.
Advantages:
1)
2)
3)
4)
This process requires less human interactions.
It can be used in various as security, medical field, detecting criminals, robots etc.
A person can be identified from a limited distance.
Has high accuracy rate.
Limitations:
1) Need special setup for the process.
2) Needs faster sampling rate.
3) Doesn’t always gives best performances at all angles.
4) Required longer training and more data.
Future work:
The scope of future research provides an insight into remote health monitoring, surveillance
and robotics. .In future,we hope,Multivariate statistical analysis and deep learning techniques
can be applied and can carry out the experiments with the recent network architectures and
novel approaches with the huge dataset of different view angles.