A combinational approach for activity recognition using context

Human action recognition is a challenging task not only because of the factors like changes in intensity, background, etc but also because of the variability in the behavioural patterns among the objects in the image which in turn affects the recognition accuracy. Analyzing all those factors and identifying the action is termed as activity recognition. In this paper, we present an approach of activity recognition with the help of context. Context can be termed as the relationship between the objects performing the activity. Activity recognition is performed based on motion identification and context information. We use Principle Component Analysis along with the low level features to perform feature extraction and then Support vector machine as a classifier which classifies the action into a class with label. Thus by performing a high level of feature extraction using context and by supervised training, we perform activity recognition and try to improve the recognition accuracy of the system.