In this paper, a linear discriminant analysis (LDA) based classifier employed in a tree structure is presented to
recognize the human actions in a wide and complex environment. In particular, the proposed classifier is based
on a supervised learning process and achieves the required classification in a multi-step process. This multi-step
process is performed simply by adopting a tree structured which is built during the training phase. Hence, there
is no need of any priori information like in other classifiers such as the number of hidden neurons or hidden
layers in a multilayer neural network based classifier or an exhaustive search as used in training algorithms
for decision trees. A skeleton based strategy is adopted to extract the features from a given video sequence
representing any human action. A Pan-Tilt-Zoom (PTZ) camera is used to monitor the wide and complex test
environment. A background mosaic image is built offline and used to compute the background images in real
time. A background subtraction strategy has been adopted for detecting the object in various frames and to
extract their corresponding silhouette. A skeleton based process is used to extract attributes of a feature vector
corresponding to a human action. Finally, the proposed framework is tested on various indoor and outdoor
scenarios and encouraging results are achieved in terms of classification accuracy.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.