{"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/hierarchical-temporal-convolution-network","title":"Hierarchical Temporal Convolution Network:Towards Privacy-Centric Activity Recognition","arxiv_id":null,"date":"2024-12-21","proceeding":"International Conference on Ubiquitous Computing and Ambient Intelligence 2024 12","authors":["Vincent Gbouna Zakka","Zhuangzhuang Dai","Luis J. Manso"],"abstract":"In response to the healthcare issues associated with the ageing population, various ambient assisted living technologies are being developed. To mitigate privacy concerns related to cloud-based data processing, recent methods have shifted towards using edge devices for local data processing. Despite their perceived benefits, the limited computational resources of these edge devices present a significant challenge for real-time performance, which is often an imperative requirement. However, recent computer vision-based methods for recognising activities of daily living among the elderly face increased computational complexity when capturing the multi-scale temporal context essential for accurate activity recognition. In this context, we propose HT-ConvNet (Hierarchical Temporal Convolution Network) to capture multi-scale temporal information without increasing computational complexity. HT-ConvNet employs exponentially increasing receptive fields across successive convolution layers to enable efficient hierarchical extraction of temporal features. Furthermore, HT-ConvNet provides an adaptive weighting mechanism to emphasise the most important features. Experimental results show that the multi-scale temporal feature extraction and the feature-weighted fusion mechanisms outperform existing methods in enhancing accuracy without increasing model complexity. The code is publicly available in: https://github.com/Gbouna/HT-ConvNet.","url_abs":"https://www.researchgate.net/publication/387230107_Hierarchical_Temporal_Convolution_Network_Towards_Privacy-Centric_Activity_Recognition","url_pdf":"https://www.researchgate.net/publication/387230107_Hierarchical_Temporal_Convolution_Network_Towards_Privacy-Centric_Activity_Recognition","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":"hierarchical-temporal-convolution-network","repo_url":"https://github.com/Gbouna/HT-ConvNet","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"activity-recognition","task_name":"Activity Recognition"},{"task_slug":"skeleton-based-action-recognition","task_name":"Skeleton Based Action Recognition"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/skeleton-based-action-recognition-on-j-hmdb","task":"Skeleton Based Action Recognition","dataset":"J-HMDB","model":"HT-ConvNet","rank_in_archive_order":11,"of":13,"metrics":{"Accuracy (RGB+pose)":"-","Accuracy (pose)":"86.1"},"uses_additional_data":false},{"leaderboard":"/sota/skeleton-based-action-recognition-on-jhmdb-2d","task":"Skeleton Based Action Recognition","dataset":"JHMDB (2D poses only)","model":"HT-ConvNet","rank_in_archive_order":1,"of":6,"metrics":{"Accuracy":"86.1","Average accuracy of 3 splits":"86.1","No. parameters":"1.75"},"uses_additional_data":false},{"leaderboard":"/sota/skeleton-based-action-recognition-on-shrec","task":"Skeleton Based Action Recognition","dataset":"SHREC 2017 track on 3D Hand Gesture Recognition","model":"HT-ConvNet","rank_in_archive_order":4,"of":7,"metrics":{"14 gestures accuracy":"97.1","28 gestures accuracy":"94.3","No. Parameters":"1.75"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}