This paper presents convolutional neural network (CNN)-based single object detection and tracking algorithms. CNN-based object detection methods are directly applicable to static images, but not to videos. On the other hand, model-free visual object tracking methods cannot detect an object until a ground truth bounding box of the target is provided. Moreover, many annotated video datasets of the target object are required to train both the object detectors and visual trackers. In this work, three simple yet effective object detection and tracking algorithms for videos are proposed to efficiently combine a state-of-the-art object detector and visual tracker for circumstances in which only a few static images of the target are available for training. The proposed algorithms are tested using a drone detection task and the experimental results demonstrated their effectiveness.

本文提出了基于卷积神经网络（CNN）的单目标检测和跟踪算法。基于CNN的对象检测方法直接适用于静态图像，但不适用于视频。另一方面，在提供目标的地面真实边界框之前，无模型的视觉对象跟踪方法无法检测到对象。此外，需要许多带注释的目标对象视频数据集来训练对象检测器和视觉跟踪器。在这项工作中，提出了三种简单而有效的视频目标检测和跟踪算法，以在只有少量目标静态图像可用于训练的情况下，有效地将最新的目标检测器和视觉跟踪器组合在一起。