Occ-Traj120: Occupancy Maps with Associated Trajectories

Trajectory modelling had been the principal research area for understanding and anticipating human behaviour. Predicting the dynamic path by observing the agent and its surrounding environment are essential for applications such as autonomous driving and indoor navigation suggestions. However, despite the numerous researches that had been presented, most available dataset does not contains any information on environmental factors---such as the occupancy representation of the map---which arguably plays a significant role on how an agent chooses its trajectory. We present a trajectory dataset with the corresponding occupancy representations of different local-maps. The dataset contains more than 120 locally-structured maps with occupancy representation and more than 110K trajectories in total. Each map has few hundred corresponding simulated trajectories that navigate from a spatial location of a room to another point. The dataset is freely available online.

[1]  Barbara Majecka,et al.  Statistical models of pedestrian behaviour in the Forum , 2009 .

[2]  Silvio Savarese,et al.  Learning Social Etiquette: Human Trajectory Understanding In Crowded Scenes , 2016, ECCV.

[3]  Tarik Kapić.  Indoor Navigation for Visually Impaired , 2003 .

[4]  Rachid Alami,et al.  Human-aware robot navigation: A survey , 2013, Robotics Auton. Syst..

[5]  H. Y. Chen,et al.  Designing and Implementing a RFID-based Indoor Guidance System , 2008 .

[6]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[7]  Luc Van Gool,et al.  You'll never walk alone: Modeling social behavior for multi-target tracking , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[8]  Dani Lischinski,et al.  Crowds by Example , 2007, Comput. Graph. Forum.

[9]  Emilio Frazzoli,et al.  Anytime Motion Planning using the RRT* , 2011, 2011 IEEE International Conference on Robotics and Automation.

[10]  Larry S. Davis,et al.  AVSS 2011 demo session: A large-scale benchmark dataset for event recognition in surveillance video , 2011, AVSS.

[11]  Fabio Ramos,et al.  Bayesian Local Sampling-Based Planning , 2020, IEEE Robotics and Automation Letters.

[12]  Terrence Fong,et al.  A Survey of Methods for Safe Human-Robot Interaction , 2017, Found. Trends Robotics.

[13]  Marco Pavone,et al.  Learning Sampling Distributions for Robot Motion Planning , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[14]  Fabio Tozeto Ramos,et al.  Balancing Global Exploration and Local-connectivity Exploitation with Rapidly-exploring Random disjointed-Trees , 2018, 2019 International Conference on Robotics and Automation (ICRA).

[15]  Albert-László Barabási,et al.  Understanding individual human mobility patterns , 2008, Nature.

[16]  Martin Lauer,et al.  A Literature Review on the Prediction of Pedestrian Behavior in Urban Scenarios , 2018, 2018 21st International Conference on Intelligent Transportation Systems (ITSC).