{"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/proxemo-gait-based-emotion-learning-and-multi","title":"ProxEmo: Gait-based Emotion Learning and Multi-view Proxemic Fusion for Socially-Aware Robot Navigation","arxiv_id":"2003.01062","date":"2020-03-02","proceeding":null,"authors":["Venkatraman Narayanan","Bala Murali Manoghar","Vishnu Sashank Dorbala","Dinesh Manocha","Aniket Bera"],"abstract":"We present ProxEmo, a novel end-to-end emotion prediction algorithm for socially aware robot navigation among pedestrians. Our approach predicts the perceived emotions of a pedestrian from walking gaits, which is then used for emotion-guided navigation taking into account social and proxemic constraints. To classify emotions, we propose a multi-view skeleton graph convolution-based model that works on a commodity camera mounted onto a moving robot. Our emotion recognition is integrated into a mapless navigation scheme and makes no assumptions about the environment of pedestrian motion. It achieves a mean average emotion prediction precision of 82.47% on the Emotion-Gait benchmark dataset. We outperform current state-of-art algorithms for emotion recognition from 3D gaits. We highlight its benefits in terms of navigation in indoor scenes using a Clearpath Jackal robot.","url_abs":"https://arxiv.org/abs/2003.01062v2","url_pdf":"https://arxiv.org/pdf/2003.01062v2.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":"proxemo-gait-based-emotion-learning-and-multi","repo_url":"https://github.com/vijay4313/proxemo","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"emotion-classification","task_name":"Emotion Classification"},{"task_slug":"emotion-recognition","task_name":"Emotion Recognition"},{"task_slug":"gesture-recognition","task_name":"Gesture Recognition"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"},{"task_slug":"robot-navigation","task_name":"Robot Navigation"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"grouped-convolution","method_name":"Grouped Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/emotion-classification-on-ewalk","task":"Emotion Classification","dataset":"EWALK","model":"ProxEmo (ours)","rank_in_archive_order":1,"of":3,"metrics":{"Accuracy":"82.4"},"uses_additional_data":false},{"leaderboard":"/sota/emotion-classification-on-ewalk","task":"Emotion Classification","dataset":"EWALK","model":"STEP [bhattacharya2019step]","rank_in_archive_order":2,"of":3,"metrics":{"Accuracy":"78.24"},"uses_additional_data":false},{"leaderboard":"/sota/emotion-classification-on-ewalk","task":"Emotion Classification","dataset":"EWALK","model":"Baseline (Vanilla LSTM) [Ewalk]","rank_in_archive_order":3,"of":3,"metrics":{"Accuracy":"55.47"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}