Real Time Extraction of Human Gait Features for Recognition

Abstract

Human motion analysis has received a great attention from researchers in the last decade due to
its potential use in different applications such as automated visual surveillance. This field of
research focuses on human activities, including people identification. Human gait is a new
biometric indicator in visual surveillance system. It can recognize individuals as the way they
walk. In the walking process, the human body shows regular periodic variation, such as upper
and lower limbs, knee point, thigh point, stride parameters (stride length, Cadence, gait cycle),
height, etc. This reflects the individual’s unique movement pattern. In gait recognition, detection
of moving people from a video is important for feature extraction. Height is one of the important
features from the several gait features which is not influenced by the camera performance,
distance and clothing style of the subject. Detection of people in video streams is the first
relevant step of information and background subtraction is a very popular approach for
foreground segmentation. In this thesis, different background subtraction methods have been
simulated to overcome the problem of illumination variation, repetitive motions from
background clutter, shadows, long term scene changes and camouflage. But background
subtraction lacks capability to remove shadows. So different shadows detection methods have
been tried out using RGB, YCbCr, and HSV color components to suppress shadows. These
methods have been simulated and quantitative performance evaluated on different indoor video
sequence. Then the research on shadow model has been extended to optimize the threshold
values of HSV color space for shadow suppression with respect to the average intensity of local
shadow region. A mathematical model is developed between the average intensity and the threshold values.Further a new method is proposed here to calculate the variation of height
during walking. The measurement of height of a person is not affected by his clothing style as
well as the distance from the camera. At any distance the height can be measured, but for that
camera calibration is essential. DLT method is used to find the height of a moving person for
each frame using intrinsic as well as extrinsic parameters. Another parameter known as stride,
function of height, is extracted using bounding box technique. As human walking style is
periodic so the accumulation of height and stride parameter will give a periodic signal. Human
identification is done by using theses parameters. The height variation and stride variation
signals are sampled to get further analyzed using DCT (Discrete Cosine Transformation), DFT
(Discrete Fourier Transformation), and DHT (Discrete Heartily Transformation) techniques. N -
harmonics are selected from the transformation coefficients. These coefficients are known as
feature vectors which are stored in the database. Euclidian distance and MSE are calculated on
these feature vectors. When feature vectors of same subject are compared, then a maximum
value of MSE is selected, known as Self-Recognition Threshold (SRT). Its value is different for
different transformation techniques. It is used to identify individuals. Again we have discussed
on Model based method to detect the thigh angle. But thigh angle of one leg can’t be detected
over a period of walking. Because one leg is occluded by the other leg. So stride parameter is
used to estimate the thigh angle.