{"type":"ScholarlyArticle","title":"Towards Fast and Accurate Multi-Person Pose Estimation on Mobile Devices","authors":[{"name":"Xuan Shen","slug":"xuan-shen","given":"Xuan","family":"Shen","affiliation":""},{"name":"Geng Yuan","slug":"geng-yuan","given":"Geng","family":"Yuan","affiliation":""},{"name":"Wei Niu","slug":"wei-niu","given":"Wei","family":"Niu","affiliation":""},{"name":"Xiaolong Ma","slug":"xiaolong-ma","given":"Xiaolong","family":"Ma","affiliation":""},{"name":"Jiexiong Guan","slug":"jiexiong-guan","given":"Jiexiong","family":"Guan","affiliation":""},{"name":"Zhengang Li","slug":"zhengang-li","given":"Zhengang","family":"Li","affiliation":""},{"name":"Bin Ren","slug":"bin-ren","given":"Bin","family":"Ren","affiliation":""},{"name":"Yanzhi Wang","slug":"yanzhi-wang","given":"Yanzhi","family":"Wang","affiliation":""}],"abstract":"The rapid development of autonomous driving, abnormal behavior detection, and behavior recognition makes an increasing demand for multi-person pose estimation-based applications, especially on mobile platforms. However, to achieve high accuracy, state-of-the-art methods tend to have a large model size and complex post-processing algorithm, which costs intense computation and long end-to-end latency. To solve this problem, we propose an architecture optimization and weight pruning framework to accelerate inference of multi-person pose estimation on mobile devices. With our optimization framework, we achieve up to 2.51X faster model inference speed with higher accuracy compared to representative lightweight multi-person pose estimator.","doi":"10.24963/ijcai.2021/715","url":"https://ijcai.org/proceedings/2021/715","pdf":"https://ijcai.org/proceedings/2021/0715.pdf","markdown":"https://ijcai.org/proceedings/2021/715.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/715","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Demo Track","track":"Demo Track","number":715,"pages":{"first":5012,"last":5015},"keywords":["Human-Computer Interaction: General","Computer Vision: General","Machine Learning: General"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}