{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/an-evaluation-of-trajectory-prediction","title":"An Evaluation of Trajectory Prediction Approaches and Notes on the TrajNet Benchmark","arxiv_id":"1805.07663","date":"2018-05-19","proceeding":null,"authors":["Stefan Becker","Ronny Hug","Wolfgang Hübner","Michael Arens"],"abstract":"In recent years, there is a shift from modeling the tracking problem based on\nBayesian formulation towards using deep neural networks. Towards this end, in\nthis paper the effectiveness of various deep neural networks for predicting\nfuture pedestrian paths are evaluated. The analyzed deep networks solely rely,\nlike in the traditional approaches, on observed tracklets without human-human\ninteraction information. The evaluation is done on the publicly available\nTrajNet benchmark dataset, which builds up a repository of considerable and\npopular datasets for trajectory-based activity forecasting. We show that a\nRecurrent-Encoder with a Dense layer stacked on top, referred to as\nRED-predictor, is able to achieve sophisticated results compared to elaborated\nmodels in such scenarios. Further, we investigate failure cases and give\nexplanations for observed phenomena and give some recommendations for\novercoming demonstrated shortcomings.","url_abs":"http://arxiv.org/abs/1805.07663v6","url_pdf":"http://arxiv.org/pdf/1805.07663v6.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"trajectory-prediction","task_name":"Trajectory Prediction"}],"methods":[],"datasets_introduced":[{"slug":"trajnet-1","name":"TrajNet","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1805.07663","atlas_url":"https://app.syntology.ai/?focus=1805.07663","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}