{"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/asynchronous-photometric-feature-tracking","title":"Asynchronous, Photometric Feature Tracking using Events and Frames","arxiv_id":"1807.09713","date":"2018-07-25","proceeding":"ECCV 2018 9","authors":["Daniel Gehrig","Henri Rebecq","Guillermo Gallego","Davide Scaramuzza"],"abstract":"We present a method that leverages the complementarity of event cameras and\nstandard cameras to track visual features with low-latency. Event cameras are\nnovel sensors that output pixel-level brightness changes, called \"events\". They\noffer significant advantages over standard cameras, namely a very high dynamic\nrange, no motion blur, and a latency in the order of microseconds. However,\nbecause the same scene pattern can produce different events depending on the\nmotion direction, establishing event correspondences across time is\nchallenging. By contrast, standard cameras provide intensity measurements\n(frames) that do not depend on motion direction. Our method extracts features\non frames and subsequently tracks them asynchronously using events, thereby\nexploiting the best of both types of data: the frames provide a photometric\nrepresentation that does not depend on motion direction and the events provide\nlow-latency updates. In contrast to previous works, which are based on\nheuristics, this is the first principled method that uses raw intensity\nmeasurements directly, based on a generative event model within a\nmaximum-likelihood framework. As a result, our method produces feature tracks\nthat are both more accurate (subpixel accuracy) and longer than the state of\nthe art, across a wide variety of scenes.","url_abs":"http://arxiv.org/abs/1807.09713v1","url_pdf":"http://arxiv.org/pdf/1807.09713v1.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":[{"paper_slug":"asynchronous-photometric-feature-tracking","repo_url":"https://github.com/uzh-rpg/rpg_eklt","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1807.09713","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}