{"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/turkergaze-crowdsourcing-saliency-with-webcam","title":"TurkerGaze: Crowdsourcing Saliency with Webcam based Eye Tracking","arxiv_id":"1504.06755","date":"2015-04-25","proceeding":null,"authors":["Pingmei Xu","Krista A. Ehinger","yinda zhang","Adam Finkelstein","Sanjeev R. Kulkarni","Jianxiong Xiao"],"abstract":"Traditional eye tracking requires specialized hardware, which means\ncollecting gaze data from many observers is expensive, tedious and slow.\nTherefore, existing saliency prediction datasets are order-of-magnitudes\nsmaller than typical datasets for other vision recognition tasks. The small\nsize of these datasets limits the potential for training data intensive\nalgorithms, and causes overfitting in benchmark evaluation. To address this\ndeficiency, this paper introduces a webcam-based gaze tracking system that\nsupports large-scale, crowdsourced eye tracking deployed on Amazon Mechanical\nTurk (AMTurk). By a combination of careful algorithm and gaming protocol\ndesign, our system obtains eye tracking data for saliency prediction comparable\nto data gathered in a traditional lab setting, with relatively lower cost and\nless effort on the part of the researchers. Using this tool, we build a\nsaliency dataset for a large number of natural images. We will open-source our\ntool and provide a web server where researchers can upload their images to get\neye tracking results from AMTurk.","url_abs":"http://arxiv.org/abs/1504.06755v2","url_pdf":"http://arxiv.org/pdf/1504.06755v2.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":"turkergaze-crowdsourcing-saliency-with-webcam","repo_url":"https://github.com/horanyinora/gazeworkshop.github.io","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"saliency-prediction","task_name":"Saliency Prediction"}],"methods":[],"datasets_introduced":[{"slug":"isun","name":"iSUN","full_name":"iSUN"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1504.06755","atlas_url":"https://app.syntology.ai/?focus=1504.06755","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}