Human Motion Parsing by Hierarchical Dynamic Clustering

Yan Zhang, Siyu Tang, He Sun, Heiko Neumann

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Parsing continuous human motion into meaningful segments plays an essential role in various applications. In this work, we propose a hierarchical dynamic clustering framework to derive action clusters from a sequence of local features in an unsupervised bottom-up manner. We systematically investigate the modules in this framework and particularly propose diverse temporal pooling schemes, in order to realize accurate temporal action localization. We demonstrate our method on two motion parsing tasks: temporal action segmentation and abnormal behavior detection. The experimental results indicate that the proposed framework is significantly more effective than the other related state-of-the-art methods on several datasets.
Original languageEnglish
Title of host publicationProceedings of the 29th British Machine Vision Conference (BMVC 2018)
Number of pages13
Publication statusPublished - 3 Sept 2018
Event29th British Machine Vision Conference (BMVC) - Northumbria University, Newcastle upon Tyne, United Kingdom
Duration: 3 Sept 20186 Sept 2018


Conference29th British Machine Vision Conference (BMVC)
Abbreviated titleBMVC 2018
Country/TerritoryUnited Kingdom
CityNewcastle upon Tyne
Internet address


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