Thermal infrared video can provide essential information about bird and bat activity for risk assessment studies, but the analysis of recorded video can be time-consuming and may not extract all of the available information. Automated processing makes continuous monitoring over extended periods of time feasible, and maximizes the information provided by video. This is especially important for collecting data in remote locations that are difficult for human observers to access, such as proposed offshore wind turbine sites. We developed new processing algorithms for single camera thermal video that automate the extraction of two-dimensional bird and bat flight tracks, and that characterize the extracted tracks to support animal identification and behavior inference. The algorithms consist of video peak store followed by background masking and perceptual grouping to extract flight tracks. The extracted tracks are automatically quantified in terms that could then be used to infer animal taxonomy and possibly behavior, as described in the companion article from Cullinan, et al. [“Classification of birds and bats using flight tracks.” Ecological Informatics, 27:55–63]. The developed automated processing was evaluated using six video clips containing a total of 184 flight tracks. The detection rate was 81% and the false positive rate was 17%. In addition to describing the details of the algorithms, we suggest models for interpreting thermal imaging information.