Slow Feature Analysis for Recognizing Prisoner’s Activities to Assist Jail Authorities

-Slow Feature Analysis (SFA) has been established as a robust and versatile technique from the neurosciences to learn slowly varying functions from quickly changing signals. SFA framework is introduced to the problem of recognizing prisoner’s actions by incorporating the supervised information with the original unsupervised SFA learning. Firstly, large amount of cuboids are collected in the motion boundaries, and local feature is described with SFA method. Each action sequence is represented by the Accumulated Squared Derivative (ASD), which is a statistical distribution of the slow features in an action sequence [1]. The descriptive statistical features are extracted inorder to reduce the dimension of the ASD feature is proposed. Finally, one against all support vector machine (SVM) is trained to classify action represented by statistical features.