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Due to the advent of new mobile devices and tracking sensors in recent years,huge amounts of data are being produced every day. Therefore, novelmethodologies need to emerge that dive through this vast sea of information andgenerate insights and meaningful information. To this end, researchers havedeveloped several trajectory classification algorithms over the years that areable to annotate tracking data. Similarly, in this research, a novelmethodology 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 toevaluate the proposed approach and compare its classification performance toother state-of-the-art trajectory classification algorithms. Experimentalresults demonstrate that TraClets achieves a classification performance that iscomparable to, or in most cases, better than the state-of-the-art, acting as auniversal, high-accuracy approach for trajectory classification.

TraClets: Harnessing the power of computer vision for trajectory classification