Recognizing User-Defined Subsequences in Human Motion Data
| Authors | |
|---|---|
| Year of publication | 2019 |
| Type | Article in Proceedings |
| Conference | International Conference on Multimedia Retrieval (ICMR) |
| MU Faculty or unit | |
| Citation | |
| Doi | http://dx.doi.org/10.1145/3323873.3326922 |
| Keywords | 3D skeleton sequence;action recognition;deep features;kNN |
| Description | Motion capture technologies digitize human movements by tracking 3D positions of specific skeleton joints in time. Such spatio-temporal multimedia data have an enormous application potential in many fields, ranging from computer animation, through security and sports to medicine, but their computerized processing is a difficult problem. In this paper, we focus on an important task of recognition of a user-defined motion, based on a collection of labelled actions known in advance. We utilize current advances in deep feature learning and scalable similarity retrieval to build an effective and efficient k-nearest-neighbor recognition technique for 3D human motion data. The properties of the technique are demonstrated by a web application which allows a user to browse long motion sequences and specify any subsequence as the input for probabilistic recognition based on 130 predefined classes. |
| Related projects: |