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This paper presents an enhancement of the standard sampling strategy for filter-based object detection and tracking in video streams. The proposed method, called staggered sampling, seeks to maximize the sampling density across video frames, thus reducing the number of patches sampled while retaining proportionally high recall rates. The method can be tailored to virtually any constraint on resources and may be used in conjunction with any filter-based object detector / tracking algorithm combination. We test our method using a modified version of the face detector in the OpenCV library and a simple tracking algorithm. The resulting detector was applied to some video sequences from the QCIF collection. Our results show that staggered sampling can achieve around 90% of the recall of full (dense) sampling while only evaluating the detector on around 10% of the image locations. At the same time the precision of the detector increases. The staggered sampling approach therefore addresses the problem of acquiring new objects in an object tracking framework by enabling a low-cost background scan of the video stream to run continuously. The simplicity and robustness of this approach make it an excellent enhancement to existing video object detection methods.