Due to the advent of new mobile devices and tracking sensors in recent years, huge amounts of data are being produced every day. Therefore, novel methodologies need to emerge that dive through this vast sea of information and generate insights and meaningful information. To this end, researchers have developed several trajectory classification algorithms over the years that are able to annotate tracking data. Similarly, in this research, a novel methodology is presented that exploits image representations of trajectories, called TraClets, in order to classify trajectories in an intuitive humans way, through computer vision techniques. Several real-world datasets are used to evaluate the proposed approach and compare its classification performance to other state-of-the-art trajectory classification algorithms. Experimental results demonstrate that TraClets achieves a classification performance that is comparable to, or in most cases, better than the state-of-the-art, acting as a universal, high-accuracy approach for trajectory classification.

翻译：由于近年来出现了新的移动装置和跟踪传感器,每天都在产生大量数据。因此,需要提出新的方法,通过这一巨大的信息海洋进行潜水,并产生洞察力和有意义的信息。为此,研究人员多年来开发了几种轨迹分类算法,能够对跟踪数据进行说明。同样,在这项研究中,提出了一种新方法,利用轨迹的图象表现,称为TraClets,以便通过计算机视觉技术,将轨迹以直观方式以人类的方式分类。一些真实世界数据集被用来评价拟议的方法,并将其分类性能与其他最先进的轨迹分类算法进行比较。实验结果表明,TraClets取得了一种与最新轨迹分类法可比的或在大多数情况下优于最新轨迹的分类性能,作为轨迹分类的普遍、高精确性方法。