Title: A Framework for view Invariant Human Identification from Video Sources
Publisher: Guru Nanak Publications
Series: Volume 3 Issue 1
Authors: Sruthy Sebastian
This paper presents a view invariant
approach to recognize humans when engaged in some
activity. Utilizing a particle swarm optimization (PSO)
and linear discriminant analysis (LDA) based
algorithm, an unknown movement is first classified,
and, then, the person performing the movement is
recognized from a movement specific person classifier.
An un-calibrated multi-camera setup is used to capture
the human body from different viewing angles. In the
case of continuous person identification in videos
depicting a person performing multiple instances of
multiple activity classes, a sliding window of
overlapping video segments consisting of video frames
is used and person identification is performed at each
window position Person identification, activity
recognition, and viewing angle specification results are
obtained for all the available cameras independently.
Human identification performance of the proposed
scheme is found to be quite good when tested on
publically available databases.
Activity recognition, Linear discriminant analysis, Particle swarm optimization, Person identification.