# Analysis and a Solution of Momentarily Missed Detection for Anchor-based Object Detectors

arXiv: https://arxiv.org/abs/1910.09212 · HTML: https://mlsanity.com/paper/1910.09212/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2019.

## Built on by (most cited first, 1 of 1)

- [A Two-Block RNN-based Trajectory Prediction from Incomplete Trajectory](https://mlsanity.com/paper/2203.07098/) (2022)

## Abstract (arXiv)

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.