Real Time Video Surveillance System » książka
Real Time Video Surveillance System
ISBN-13: 9783659541902 / Angielski / Miękka / 2014 / 112 str.
The significance of Real Time Video Surveillance System has been elevated due to increased importance in safety and security. The Real Time Video Surveillance System is capable of detecting, classifying and tracking objects of interest (human), by recognizing motion of a walking person in video, which can lead to a useful system. This is done by generating notification to an authoritative person by displaying the recorded face of the threat. Gait Analysis helps us to identify a person by their walking style, which also helps for individual recognition i.e. for performing feature extraction. To accomplish this we have used a new spatio-temporal gait representation called as Gait Energy Image (GEI). GEI represents a human motion sequence in a single image while preserving temporal information. For human recognition, statistical GEI feature matching is used to overcome limitation of training templates. Also to reduce dimensionality problem of GEI's, we have used two approaches, namely Principal Component Analysis (PCA) and its variants Multiple Discriminant Analysis (MDA). These approaches helped us to achieve good performance over the existing approaches.
The significance of Real Time Video Surveillance System has been elevated due to increased importance in safety and security. The Real Time Video Surveillance System is capable of detecting, classifying and tracking objects of interest (human), by recognizing motion of a walking person in video, which can lead to a useful system. This is done by generating notification to an authoritative person by displaying the recorded face of the threat. Gait Analysis helps us to identify a person by their walking style, which also helps for individual recognition i.e. for performing feature extraction. To accomplish this we have used a new spatio-temporal gait representation called as Gait Energy Image (GEI). GEI represents a human motion sequence in a single image while preserving temporal information. For human recognition, statistical GEI feature matching is used to overcome limitation of training templates. Also to reduce dimensionality problem of GEIs, we have used two approaches, namely Principal Component Analysis (PCA) and its variants Multiple Discriminant Analysis (MDA). These approaches helped us to achieve good performance over the existing approaches.
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