The ability to track moving objects in a video stream is helpful for many applications,from pedestrian and vehicle tracking in a city to animal tracking for ecology and conservation. This write-up introduces Spot, an algorithm for moving object tracking in low-resolution, low-contrast videos. This write-up will discuss two motivating examples to guide the development of Spot–satellite-based surveillance of vehicles in cityscapes and animal tracking using drones for ecological purposes. Spot uses image processing techniques to generate a pipeline to track moving objects frame-to-frame. It then leverages Bayesian Filtering techniques to use the frame-to-frame motion xito track individual identity between consecutive frames. Each stage of Spot’s pipeline–both image processing and the Bayesian Filtering portions of the pipeline–introduces many parameters. To determine which parameters are ideal for a particular dataset, a design space exploration tool, dubbed Sherlock, is used to choose the optimal parameters. As part of this, we evaluate multiple possible objective functions and demonstrate the importance of selecting an appropriate one. Spot is competitive with other modern, moving object-tracking algorithms on cityscape data, outperforming others in some of the metrics presented. For tracking animals from drone footage, Spot demonstrated an ability to track wildlife at a similar rate to its performance in some cityscape videos.