  The employment of convolutional neural networks has led to significant
performance improvement on the task of object detection. However, when applying
existing detectors to continuous frames in a video, we often encounter
momentary miss-detection of objects, that is, objects are undetected
exceptionally at a few frames, although they are correctly detected at all
other frames. In this paper, we analyze the mechanism of how such
miss-detection occurs. For the most popular class of detectors that are based
on anchor boxes, we show the followings: i) besides apparent causes such as
motion blur, occlusions, background clutters, etc., the majority of remaining
miss-detection can be explained by an improper behavior of the detectors at
boundaries of the anchor boxes; and ii) this can be rectified by improving the
way of choosing positive samples from candidate anchor boxes when training the
detectors.
