Abstract
Human Trajectory Prediction (HTP) has gained much momentum in the last years and many solutions have been proposed to solve it. Proper benchmarking being a key issue for comparing methods, this paper addresses the question of evaluating how complex is a given dataset with respect to the prediction problem. For assessing a dataset complexity, we define a series of indicators around three concepts: Trajectory predictability; Trajectory regularity; Context complexity. We compare the most common datasets used in HTP in the light of these indicators and discuss what this may imply on benchmarking of HTP algorithms. Our source code is released on Github (https://github.com/crowdbotp/OpenTraj).
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Acknowledgements
This research is supported by the CrowdBot H2020 EU Project http://crowdbot.eu and by the Intel Probabilistic Computing initiative. The work done by Francisco Valente Castro was sponsored using an MSc Scholarship given by CONACYT with the following scholar registry number 1000188.
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Amirian, J., Zhang, B., Castro, F.V., Baldelomar, J.J., Hayet, JB., Pettré, J. (2021). OpenTraj: Assessing Prediction Complexity in Human Trajectories Datasets. In: Ishikawa, H., Liu, CL., Pajdla, T., Shi, J. (eds) Computer Vision – ACCV 2020. ACCV 2020. Lecture Notes in Computer Science(), vol 12627. Springer, Cham. https://doi.org/10.1007/978-3-030-69544-6_34
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