1 research outputs found
Detection, Recognition and Tracking of Moving Objects from Real-time Video via SP Theory of Intelligence and Species Inspired PSO
In this paper, we address the basic problem of recognizing moving objects in
video images using SP Theory of Intelligence. The concept of SP Theory of
Intelligence which is a framework of artificial intelligence, was first
introduced by Gerard J Wolff, where S stands for Simplicity and P stands for
Power. Using the concept of multiple alignment, we detect and recognize object
of our interest in video frames with multilevel hierarchical parts and
subparts, based on polythetic categories. We track the recognized objects using
the species based Particle Swarm Optimization (PSO). First, we extract the
multiple alignment of our object of interest from training images. In order to
recognize accurately and handle occlusion, we use the polythetic concepts on
raw data line to omit the redundant noise via searching for best alignment
representing the features from the extracted alignments. We recognize the
domain of interest from the video scenes in form of wide variety of multiple
alignments to handle scene variability. Unsupervised learning is done in the SP
model following the DONSVIC principle and natural structures are discovered via
information compression and pattern analysis. After successful recognition of
objects, we use species based PSO algorithm as the alignments of our object of
interest is analogues to observation likelihood and fitness ability of species.
Subsequently, we analyze the competition and repulsion among species with
annealed Gaussian based PSO. We have tested our algorithms on David, Walking2,
FaceOcc1, Jogging and Dudek, obtaining very satisfactory and competitive
results