{"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/batch-based-activity-recognition-from-1","title":"Batch-based Activity Recognition from Egocentric Photo-Streams Revisited","arxiv_id":"1710.04112","date":"2017-10-11","proceeding":null,"authors":["Alejandro Cartas","Juan Marin","Petia Radeva","Mariella Dimiccoli"],"abstract":"Wearable cameras can gather large a\\-mounts of image data that provide rich\nvisual information about the daily activities of the wearer. Motivated by the\nlarge number of health applications that could be enabled by the automatic\nrecognition of daily activities, such as lifestyle characterization for habit\nimprovement, context-aware personal assistance and tele-rehabilitation\nservices, we propose a system to classify 21 daily activities from\nphoto-streams acquired by a wearable photo-camera. Our approach combines the\nadvantages of a Late Fusion Ensemble strategy relying on convolutional neural\nnetworks at image level with the ability of recurrent neural networks to\naccount for the temporal evolution of high level features in photo-streams\nwithout relying on event boundaries. The proposed batch-based approach achieved\nan overall accuracy of 89.85\\%, outperforming state of the art end-to-end\nmethodologies. These results were achieved on a dataset consists of 44,902\negocentric pictures from three persons captured during 26 days in average.","url_abs":"http://arxiv.org/abs/1710.04112v2","url_pdf":"http://arxiv.org/pdf/1710.04112v2.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":"batch-based-activity-recognition-from-1","repo_url":"https://github.com/gorayni/egocentric_photostreams","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"activity-recognition","task_name":"Activity Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}