A sequential vehicle classifier for infrared video using multinomial pattern matching.

Conference · Sun Jan 01 04:00:00 EST 2006

OSTI ID:947314

Malone, Kevin Thomas; Koch, Mark William

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.

• Research Organization: Sandia National Laboratories
• Sponsoring Organization: USDOE
• DOE Contract Number: AC04-94AL85000
• OSTI ID: 947314
• Report Number(s): SAND2006-0546C
• Country of Publication: United States
• Language: English

Related Subjects

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION
ALGORITHMS
CLASSIFICATION
DETECTION
INFRARED RADIATION
PATTERN RECOGNITION
PERFORMANCE
VEHICLES