TITLE:
Robust Pre-Attentive Attention Direction Using Chaos Theory for Video Surveillance
AUTHORS:
Michael E. Farmer
KEYWORDS:
Image Change Detection; Image Sequence Analysis; Chaos; Fractals; Nonlinearities
JOURNAL NAME:
Applied Mathematics,
Vol.4 No.9A,
September
30,
2013
ABSTRACT:
Attention direction for active vision systems has been
of substantial interest in the image processing and computer vision communities
for video surveillance. Biological vision systems have been shown to possess a
hierarchical structure where a pre-attentive processing function directs the
visual attention to regions of interest which are then possibly further
processed by higher-level vision functions. Biological neural systems are also
highly responsive to signals which appear to be chaotic in nature. In this
paper we explore applying measures from chaos theory and fractal analysis to
provide a robust pre-attentive processing engine for vision. The approach is
applied to two standard data sets related to video surveillance for detecting
bags left suspiciously in public places. Results compare quite favorably in
terms of probability of detection versus false detection rate shown through
Receiver Operating Characteristic (ROC) curves against two traditional methods
for low-level change detection, namely Mutual Information, Sum of Absolute Differences,
and Gaussian Mixture Models.