Object pose detection and tracking has recently attracted increasing attention due to its wide applications in many areas, such as autonomous driving, robotics, and augmented reality. Among methods for object pose detection and tracking, deep learning is the most promising one that has shown better performance than others. However, there is lack of survey study about latest development of deep learning based methods. Therefore, this paper presents a comprehensive review of recent progress in object pose detection and tracking that belongs to the deep learning technical route. To achieve a more thorough introduction, the scope of this paper is limited to methods taking monocular RGB/RGBD data as input, covering three kinds of major tasks: instance-level monocular object pose detection, category-level monocular object pose detection, and monocular object pose tracking. In our work, metrics, datasets, and methods about both detection and tracking are presented in detail. Comparative results of current state-of-the-art methods on several publicly available datasets are also presented, together with insightful observations and inspiring future research directions.
翻译:最近,由于物体的探测和跟踪在许多领域(如自主驱动、机器人和增强现实)的广泛应用,最近引起越来越多的注意。物体的探测和跟踪方法包括:物体的探测和跟踪,深层次的学习是最有希望的,但缺乏关于深层学习方法最新发展情况的调查研究。因此,本文件对物体的探测和跟踪最新进展进行了全面审查,这些进展属于深层学习技术途径。为了更彻底的介绍,本文件的范围限于将单项RGB/RGBBD数据作为投入的方法,涵盖三种主要任务:如单项单项物体的探测、类级单项物体的检测和单项物体的跟踪。在我们的工作中,还详细介绍了关于探测和跟踪的衡量标准、数据集和方法。还介绍了关于若干公开数据集的当前最新方法的比较结果,同时提出了深刻的观察和令人鼓舞的未来研究方向。