TraClets: Harnessing the power of computer vision for trajectory classification
This provides a high-accuracy, universal approach for classifying trajectories from mobile and sensor data, addressing the need to process large volumes of tracking information, though it is incremental as it builds on existing trajectory classification algorithms.
The paper tackles trajectory classification by converting trajectories into image representations called TraClets and applying computer vision techniques, achieving performance comparable to or better than state-of-the-art methods on real-world datasets.
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.