TBD Pedestrian Data Collection: Towards Rich, Portable, and Large-Scale Natural Pedestrian Data
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866916455397720064 |
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| author | Wang, Allan Sato, Daisuke Corzo, Yasser Simkin, Sonya Biswas, Abhijat Steinfeld, Aaron |
| author_facet | Wang, Allan Sato, Daisuke Corzo, Yasser Simkin, Sonya Biswas, Abhijat Steinfeld, Aaron |
| contents | Social navigation and pedestrian behavior research has shifted towards machine learning-based methods and converged on the topic of modeling inter-pedestrian interactions and pedestrian-robot interactions. For this, large-scale datasets that contain rich information are needed. We describe a portable data collection system, coupled with a semi-autonomous labeling pipeline. As part of the pipeline, we designed a label correction web app that facilitates human verification of automated pedestrian tracking outcomes. Our system enables large-scale data collection in diverse environments and fast trajectory label production. Compared with existing pedestrian data collection methods, our system contains three components: a combination of top-down and ego-centric views, natural human behavior in the presence of a socially appropriate "robot", and human-verified labels grounded in the metric space. To the best of our knowledge, no prior data collection system has a combination of all three components. We further introduce our ever-expanding dataset from the ongoing data collection effort -- the TBD Pedestrian Dataset and show that our collected data is larger in scale, contains richer information when compared to prior datasets with human-verified labels, and supports new research opportunities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_17187 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | TBD Pedestrian Data Collection: Towards Rich, Portable, and Large-Scale Natural Pedestrian Data Wang, Allan Sato, Daisuke Corzo, Yasser Simkin, Sonya Biswas, Abhijat Steinfeld, Aaron Computer Vision and Pattern Recognition Human-Computer Interaction Robotics Social navigation and pedestrian behavior research has shifted towards machine learning-based methods and converged on the topic of modeling inter-pedestrian interactions and pedestrian-robot interactions. For this, large-scale datasets that contain rich information are needed. We describe a portable data collection system, coupled with a semi-autonomous labeling pipeline. As part of the pipeline, we designed a label correction web app that facilitates human verification of automated pedestrian tracking outcomes. Our system enables large-scale data collection in diverse environments and fast trajectory label production. Compared with existing pedestrian data collection methods, our system contains three components: a combination of top-down and ego-centric views, natural human behavior in the presence of a socially appropriate "robot", and human-verified labels grounded in the metric space. To the best of our knowledge, no prior data collection system has a combination of all three components. We further introduce our ever-expanding dataset from the ongoing data collection effort -- the TBD Pedestrian Dataset and show that our collected data is larger in scale, contains richer information when compared to prior datasets with human-verified labels, and supports new research opportunities. |
| title | TBD Pedestrian Data Collection: Towards Rich, Portable, and Large-Scale Natural Pedestrian Data |
| topic | Computer Vision and Pattern Recognition Human-Computer Interaction Robotics |
| url | https://arxiv.org/abs/2309.17187 |