Vehicle detection approaches using the NVESD Sensor Fusion Testbed

Abstract

The US Army RDECOM CERDEC Night Vision & Electronic Sensors Directorate (NVESD) has a dynamic applied research program in sensor fusion for a wide variety of defense & defense related applications. This paper highlights efforts under the NVESD Sensor Fusion Testbed (SFTB) in the area of detection of moving vehicles with a network of image and acoustic sensors. A sensor data collection was designed and conducted using a variety of vehicles. Data from this collection included signature data of the vehicles as well as moving scenarios. Sensor fusion for detection and classification is performed at both the sensor level and the feature level, providing a basis for making tradeoffs between performance desired and resources required. Several classifier types are examined (parametric, nonparametric, learning). The combination of their decisions is used to make the final decision.