Abstract The unrelenting threat of poaching has led to increased development of new technologies to combat it. One such example is the use of thermal infrared cameras mounted on unmanned aerial vehicles (UAVs or drones) to spot poachers at night and report them to park rangers before they are able to harm any animals. However, monitoring the live video stream from these conservation UAVs all night is an arduous task. Therefore, we discuss SPOT (Systematic POacher deTector), a novel application that augments conservation drones with the ability to automatically detect poachers and animals in near real time [Bondi et al., 2018b]. SPOT illustrates the feasibility of building upon state-of-the-art AI techniques, such as Faster RCNN, to address the challenges of automatically detecting animals and poachers in infrared images. This paper reports (i) the design of SPOT, (ii) efficient processing techniques to ensure usability in the field, (iii) evaluation of SPOT based on historical videos and a real-world test run by the end-users, Air Shepherd, in the field, and (iv) the use of AirSim for live demonstration of SPOT. The promising results from a field test have led to a plan for larger-scale deployment in a national park in southern Africa. While SPOT is developed for conservation drones, its design and novel techniques have wider application for automated detection from UAV videos.