Motion Data Processing

DEMO #1: Subsequence search

Subsequence search task: given a short query, search for similar subsequences within long motions. A total motion sequence of 12 hours can be searched. A short query motion is specified by the user using the query-by-example paradigm. Query-similar sub-motions are retrieved and displayed, and ordered according to their similarity score. A new query can be selected from the retrieved results. Try the subseqeunce search demo

DEMO #2: Action recognition

Classification task: recognize labels of any user-selected short motion. A short motion (sub)sequence is selected as a query. The retrieval engine compares the query with 2K+ categorized motion samples to obtain the ranked list of nearest matches from which the most probable action label is obtained. The demonstration currently supports 130 label categories.

LSMB19: Dataset for benchmarking search and annotation

We propose a benchmark to evaluate search and annotation algorithms. The benchmark contains a motion dataset of 2 very long and continuous unsegmented 3D skeleton sequences, training and testing data for two modalities (cross-subject and cross view), 98 search queries and ground truth labels. Visit LSMB19 benchmark homepage

Related Publications

• J. Sedmidubsky, P. Elias, P. Budikova, P. Zezula: Content-Based Management of Human Motion Data: Survey and Challenges. IEEE Access. IEEE, 2021. doi:10.1109/ACCESS.2021.3075766.
• P. Budikova, J. Sedmidubsky, J. Horvath, P. Zezula: Towards Scalable Retrieval of Human Motion Episodes. In IEEE International Symposium on Multimedia (ISM). IEEE, 2020. doi:10.1109/ISM50513.2020.
• J. Sedmidubsky, P. Budikova, V. Dohnal, P. Zezula: Motion Words: A Text-like Representation of 3D Skeleton Sequences. In 42nd European Conference on Information Retrieval (ECIR). Cham: Springer, 2020. doi:10.1007/978-3-030-45439-5_35.
• J. Sedmidubsky, P. Elias, P. Zezula: Searching for Variable-Speed Motions in Long Sequences of Motion Capture Data. Information Systems, Elsevier, 2019. doi:10.1016/j.is.2018.04.002.
• F. Carrara, P. Elias, J. Sedmidubsky, P. Zezula: LSTM-Based Real-Time Action Detection and Prediction in Human Motion Streams. Multimedia Tools and Applications, Springer US, 2019. doi:10.1007/s11042-019-07827-3.
• J. Sedmidubsky, P. Elias, P. Zezula: Benchmarking Search and Annotation in Continuous Human Skeleton Sequences. In International Conference on Multimedia Retrieval (ICMR 2019). New York, NY, USA: ACM, 2019. doi:10.1145/3323873.3325013.
• J. Sedmidubsky, P. Elias, P. Zezula: Effective and Efficient Similarity Searching in Motion Capture Data. Multimedia Tools and Applications, Springer US, 2018. doi:10.1007/s11042-017-4859-7.
• J. Valcik, J. Sedmidubsky, P. Zezula: Assessing similarity models for human-motion retrieval applications. Computer Animation and Virtual Worlds, John Wiley & Sons Ltd. ISSN 1546-427X, 2015. doi: 10.1002/cav.1674.
• J. Sedmidubsky, J. Valcik, P. Zezula: A Key-Pose Similarity Algorithm for Motion Data Retrieval. In 12th International Conference on Advanced Concepts for Intelligent Vision Systems (ACIVS 2013). Springer-Verlag, 2013. doi: 10.1007/978-3-319-02895-8_60.