{"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/large-scale-isolated-gesture-recognition","title":"Large-scale Isolated Gesture Recognition Using Convolutional Neural Networks","arxiv_id":"1701.01814","date":"2017-01-07","proceeding":null,"authors":["Pichao Wang","Wanqing Li","Song Liu","Zhimin Gao","Chang Tang","Philip Ogunbona"],"abstract":"This paper proposes three simple, compact yet effective representations of\ndepth sequences, referred to respectively as Dynamic Depth Images (DDI),\nDynamic Depth Normal Images (DDNI) and Dynamic Depth Motion Normal Images\n(DDMNI). These dynamic images are constructed from a sequence of depth maps\nusing bidirectional rank pooling to effectively capture the spatial-temporal\ninformation. Such image-based representations enable us to fine-tune the\nexisting ConvNets models trained on image data for classification of depth\nsequences, without introducing large parameters to learn. Upon the proposed\nrepresentations, a convolutional Neural networks (ConvNets) based method is\ndeveloped for gesture recognition and evaluated on the Large-scale Isolated\nGesture Recognition at the ChaLearn Looking at People (LAP) challenge 2016. The\nmethod achieved 55.57\\% classification accuracy and ranked $2^{nd}$ place in\nthis challenge but was very close to the best performance even though we only\nused depth data.","url_abs":"http://arxiv.org/abs/1701.01814v1","url_pdf":"http://arxiv.org/pdf/1701.01814v1.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":"classification","task_name":"General Classification"},{"task_slug":"gesture-recognition","task_name":"Gesture Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/hand-gesture-recognition-on-chalearn-val","task":"Hand Gesture Recognition","dataset":"ChaLearn val","model":"Wang et al.","rank_in_archive_order":2,"of":3,"metrics":{"Accuracy":"39.23"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1701.01814","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}