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Object Detection (OD) is an important task in Computer Vision with manypractical applications. For some use cases, OD must be done on videos, wherethe object of interest has a periodic motion. In this paper, we formalize theproblem of periodic OD, which consists in improving the performance of an ODmodel in the specific case where the object of interest is repeating similarspatio-temporal trajectories with respect to the video frames. The proposedapproach is based on training a Gaussian Process to model the periodic motion,and use it to filter out the erroneous predictions of the OD model. Bysimulating various OD models and periodic trajectories, we demonstrate thatthis filtering approach, which is entirely data-driven, improves the detectionperformance by a large margin.

Robust Detection of Objects under Periodic Motion with Gaussian Process Filtering