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

Yusuke Hosoya, Masanori Suganuma, Takayuki Okatani; The IEEE Winter Conference on Applications of Computer Vision (WACV), 2020, pp. 1410-1418

Abstract


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.

Related Material


[pdf]
[bibtex]
@InProceedings{Hosoya_2020_WACV,
author = {Hosoya, Yusuke and Suganuma, Masanori and Okatani, Takayuki},
title = {Analysis and a Solution of Momentarily Missed Detection for Anchor-based Object Detectors},
booktitle = {The IEEE Winter Conference on Applications of Computer Vision (WACV)},
month = {March},
year = {2020}
}