Paper Info
Title
A Sequential Vehicle Classifier for Infrared Video using Multinomial Pattern Matching
Abstract
Vehicle classification is a challenging problem, since vehicles can take on many different appearances and sizes due to their form and function, and the viewing conditions. The low resolution of uncooled-infrared video and the large variability of naturally occurring environmental conditions can make this an even more difficult problem. We develop a multilook fusion approach for improving the performance of a single look system. Our single look approach is based on extracting a signature consisting of a histogram of gradient orientations from a set of regions covering the moving object. We use the multinomial pattern matching algorithm to match the signature to a database of learned signatures. To combine the match scores of multiple signatures from a single tracked object, we use the sequential probability ratio test. Using real infrared data we show excellent classification performance, with low expected error rates, when using at least 25 looks.
Year
DOI
Venue
2006
10.1109/CVPRW.2006.21
CVPR Workshops
Field
DocType
Volume
String searching algorithm,Computer vision,Histogram,Object detection,Radar tracker,Pattern recognition,Computer science,Multinomial distribution,Artificial intelligence,Classifier (linguistics),Pattern matching,Sequential probability ratio test
Conference
2006
Issue
ISSN
ISBN
1
2160-7508
0-7695-2646-2
Citations 
PageRank 
References 
3
0.48
5
Authors
2
Name
Order
Citations
PageRank
Mark W. Koch19210.60
Kevin T. Malone230.48