School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, China.
Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province, Chengdu 611731, China.
Sensors (Basel). 2022 Mar 2;22(5):1967. doi: 10.3390/s22051967.
This paper proposes an image augmentation model of limited samples on the mobile platform for object tracking. The augmentation method mainly aims at the detection failure caused by the small number of effective samples, jitter of tracking platform, and relative rotation between camera and object in the tracking process. Aiming at the object tracking problem, we first propose to use geometric projection transformation, multi-directional overlay blurring, and random background filling to improve the generalization ability of samples. Then, selecting suitable traditional augmentation methods as the supplements, an image augmentation model with an adjustable probability factor is provided to simulate various kinds of samples to help the detection model carry out more reliable training. Finally, combined with a spatial localization algorithm based on geometric constraints proposed by the author's previous work, a framework for object tracking with an image augmentation method is proposed. SSD, YOLOv3, YOLOv4, and YOLOx are adopted in the experiment of this paper as the detection models. And a large number of object recognition and object tracking experiments are carried out by combining with common data sets OTB50 and OTB100 as well as the OTMP data set proposed by us for mobile platform. The augmented module proposed in this paper is conducive for the detection model to improve the detection accuracy by at least 10%. Especially for objects with planar characteristics, the affine and projection transformation used in this paper can greatly improve the detection accuracy of the model. Based on the object tracking framework of our augmented model, the RMSE is estimated to be less than 4.21 cm in terms of the actual tracking of indoor objects.
本文提出了一种在移动平台上针对目标跟踪的有限样本图像增强模型。增强方法主要针对跟踪过程中由于有效样本数量少、跟踪平台抖动以及相机与目标之间相对旋转而导致的检测失败问题。针对目标跟踪问题，我们首先提出使用几何投影变换、多方向叠加模糊和随机背景填充来提高样本的泛化能力。然后，选择合适的传统增强方法作为补充，提供一个具有可调概率因子的图像增强模型，以模拟各种样本，帮助检测模型进行更可靠的训练。最后，结合作者之前工作提出的基于几何约束的空间定位算法，提出了一种带有图像增强方法的目标跟踪框架。本文的实验采用了 SSD、YOLOv3、YOLOv4 和 YOLOx 作为检测模型，并结合常见数据集 OTB50 和 OTB100 以及我们为移动平台提出的 OTMP 数据集进行了大量的目标识别和目标跟踪实验。本文提出的增强模块有助于检测模型提高至少 10%的检测精度。特别是对于具有平面特征的物体，本文中使用的仿射和投影变换可以大大提高模型的检测精度。基于我们增强模型的目标跟踪框架，实际跟踪室内物体时 RMSE 估计值小于 4.21cm。
