Abstract:
Vision-based gait recognition has excellent potential for biometric
identification due to its non-intrusive, non-invasive and remote access
person
data collection. Gait recognition has diverse applications for visual
surveillance due to its adaptability for person identification and making
predictions about age, gender, and ethnic background. The vision-based gait
recognition is adaptable on low-resolution video as it only lequires the
visibility of the human body for feature extraction. The vision-based gait
recognition-based person identification is highly affected by the factors
altering the perceivable shape of the human body, including variance in the
subject’s appearance and viewing angle. These factors reduce the adaptability
variance
of conventional gait feature extraction techniques, including Gait Energy
Image and Gait Silhouette. The problem of gait recognition robust to
appearance variance is twofold complex as it introduces higher intra class
and lower inter-class variance. These two problems require
developing gait features that strongly correlate within the same class and
discriminant enough for multi-class classification. This thesis proposes gait
feature extraction technique named “Dynamic Gait Feature”, by estimating
the relative motion between key poses of the gait cycle and encoding it as
feature vectors. The Dynamic Gait Features are evaluated on dual criteria of
the problem and are established to be strongly correlated within class and
adaptable with Support Vector Machine classifier-based gait recognition.
These Dynamic Gait Features are further transformed into Spatio Temporal
Power Spectral (STPS) gait features. The robustness of STPS gait features
towards different, appearances and views is established by adapting machine
learning classifiers. The Dynamic Gait Feature based gait recognition has
achieved 97.53% accuracy despite significant appearance variance. The STPS based gait recognition has achieved 99.87% accuracy despite view and
appearance variance. This thesis also addressed the effects of south Asian
clothing on the subject’s appearance. A local dataset has been developed to
address the effects of south Asian clothing on the subject’s appearance. We
expect this research to set a new dimension for the adaptation of vision-based
gait recognition for automated visual surveillance that is adaptable in a real
time environment.
