Near infrared (NIR) cameras, with peak sensitivity around 905-nm wavelengths, are increasingly used in object detection
applications such as pedestrian detection, occupant detection in vehicles, and vehicle detection. In this work, we present
the results of simulated sensitivity analysis for object detection with NIR cameras. The analysis was conducted using
high performance computing (HPC) to determine the environmental effects on object detection in different terrains and
environmental conditions. The Virtual Autonomous Navigation Environment (VANE) was used to simulate highresolution
models for environment, terrain, vehicles, and sensors.
In the experiment, an active fiducial marker was attached to the rear bumper of a vehicle. The camera was mounted on a
following vehicle that trailed at varying standoff distances. Three different terrain conditions (rural, urban, and forest),
two environmental conditions (clear and hazy), three different times of day (morning, noon, and evening), and six
different standoff distances were used to perform the sensor sensitivity analysis. The NIR camera that was used for the
simulation is the DMK firewire monochrome on a pan-tilt motor.
Standoff distance was varied along with environment and environmental conditions to determine the critical failure
points for the sensor. Feature matching was used to detect the markers in each frame of the simulation, and the
percentage of frames in which one of the markers was detected was recorded. The standoff distance produced the biggest
impact on the performance of the camera system, while the camera system was not sensitive to environment conditions.