Search models based on human perception have been developed by military researchers over the past few decades and have both military and commercial applications for sensor design and implementation. These models are created primarily for static imagery, and accurately predict task performance for systems with stationary targets and stationary sensors, if the observer is given infinite time to make targeting decisions. To account for situations where decisions must be made on a shortened time scale, the time-limited search model was developed to describe how task performance evolves with time. Recent variations of this model have been made to account for dynamic target situations and dynamic sensor situations. The latter of these was designed to model performance from vehicle-mounted sensors. This model has been applied here for the optimization of sensor configuration for near-infrared search of Burmese pythons in grass, for both static imagery and for videos recorded from a moving sensor platform. By coupling the established dynamic sensor model with camera matrix theory, measured static human perception data can be used to optimize sensing system selection and sensor operations including sensor pointing angle, height, and platform speed to maximize human search performance for the detection of close-range ground targets from a moving sensor platform. To illustrate this, this methodology is applied to the detection of Burmese pythons viewed in near-infrared from a moving sensor platform.