OpenTraj: Assessing Prediction Complexity in Human Trajectories Datasets

• URL: http://arxiv.org/abs/2010.00890v2
• Date: Mon, 2 Nov 2020 21:24:33 GMT
• Title: OpenTraj: Assessing Prediction Complexity in Human Trajectories Datasets
• Authors: Javad Amirian, Bingqing Zhang, Francisco Valente Castro, Juan Jose Baldelomar, Jean-Bernard Hayet and Julien Pettre
• Abstract summary: Human Trajectory Prediction (HTP) has gained much momentum in the last years and many solutions have been proposed to solve it. 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.
• Score: 5.219568203653524
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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