Abstract
Successive frames of a video are highly redundant, and the most popular object detection methods do not take advantage of this fact. Using multiple consecutive frames can improve detection of small objects or difficult examples and can improve speed and detection consistency in a video sequence, for instance by interpolating features between frames. In this work, a novel approach is introduced to perform online video object detection using two consecutive frames of video sequences involving road users. Two new models, RetinaNet-Double and RetinaNet-Flow, are proposed, based respectively on the concatenation of a target frame with a preceding frame, and the concatenation of the optical flow with the target frame. The models are trained and evaluated on three public datasets. Experiments show that using a preceding frame improves performance over single frame detectors, but using explicit optical flow usually does not.
Abstract (translated)
连续的视频帧是高度冗余的,最流行的目标检测方法没有利用这一事实。使用多个连续帧可以提高对小对象或困难示例的检测,并可以提高视频序列中的速度和检测一致性,例如通过在帧之间插入特征。本文介绍了一种利用两帧连续视频序列对道路使用者进行在线视频目标检测的新方法。基于目标帧与前一帧的级联,以及光流与目标帧的级联,分别提出了两种新的模型:retinanet double和retinanet flow。模型在三个公共数据集上进行训练和评估。实验表明,使用前一帧可以提高单帧探测器的性能,但使用显式光流通常不能。
URL
https://arxiv.org/abs/1903.12049