Name
Abstract | ||
|---|---|---|
We study the problem of classifying actions of human subjects using depth movies generated by Kinect or other depth sensors. Representing human body as dynamical skeletons, we study the evolution of their (skeletons’) shapes as trajectories on Kendall’s shape manifold. The action data is typically corrupted by large variability in execution rates within and across subjects and, thus, causing major problems in statistical analyses. To address that issue, we adopt a recently-developed framework of Su et al. [1], [2] to this problem domain. Here, the variable execution rates correspond to re-parameterizations of trajectories, and one uses a parameterization-invariant metric for aligning, comparing, averaging, and modeling trajectories. This is based on a combination of transported square-root vector fields (TSRVFs) of trajectories and the standard Euclidean norm, that allows computational efficiency. We develop a comprehensive suite of computational tools for this application domain: smoothing and denoising skeleton trajectories using median filtering, up- and down-sampling actions in time domain, simultaneous temporalregistration of multiple actions, and extracting invertible Euclidean representations of actions. Due to invertibility these Euclidean representations allow both discriminative and generative models for statistical analysis. For instance, they can be used in a SVM-based classification of original actions as demonstrated here using MSR Action-3D, MSR Daily Activity and 3D Action Pairs datasets. This approach, using only the skeletal data, achieves the state-of-the-art classification results on these datasets. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TPAMI.2015.2439257 | Pattern Analysis and Machine Intelligence, IEEE Transactions |
Keywords | Field | DocType |
action recognition,depth sensors,manifold trajectories,riemannian geometry,skeletal data,shape,trajectory,hidden markov models,skeleton,measurement | Computer vision,Pattern recognition,Problem domain,Computer science,Euclidean distance,Support vector machine,Smoothing,Artificial intelligence,Invariant (mathematics),Hidden Markov model,Discriminative model,Manifold | Journal |
Volume | Issue | ISSN |
PP | 99 | 0162-8828 |
Citations | PageRank | References |
73 | 1.34 | 31 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ben Amor, B. | 1 | 212 | 5.98 |
| Jing-yong Su | 2 | 156 | 10.93 |
| Anuj Srivastava | 3 | 2853 | 199.47 |