Name
Title | ||
|---|---|---|
Video-Based Action Recognition Using Rate-Invariant Analysis of Covariance Trajectories. |
Abstract | ||
|---|---|---|
Statistical classification of actions in videos is mostly performed by extracting relevant features, particularly covariance features, from image frames and studying time series associated with temporal evolutions of these features. A natural mathematical representation of activity videos is in form of parameterized trajectories on the covariance manifold, i.e. the set of symmetric, positive-definite matrices (SPDMs). The variable execution-rates of actions implies variable parameterizations of the resulting trajectories, and complicates their classification. Since action classes are invariant to execution rates, one requires rate-invariant metrics for comparing trajectories. A recent paper represented trajectories using their transported square-root vector fields (TSRVFs), defined by parallel translating scaled-velocity vectors of trajectories to a reference tangent space on the manifold. To avoid arbitrariness of selecting the reference and to reduce distortion introduced during this mapping, we develop a purely intrinsic approach where SPDM trajectories are represented by redefining their TSRVFs at the starting points of the trajectories, and analyzed as elements of a vector bundle on the manifold. Using a natural Riemannain metric on vector bundles of SPDMs, we compute geodesic paths and geodesic distances between trajectories in the quotient space of this vector bundle, with respect to the re-parameterization group. This makes the resulting comparison of trajectories invariant to their re-parameterization. We demonstrate this framework on two applications involving video classification: visual speech recognition or lip-reading and hand-gesture recognition. In both cases we achieve results either comparable to or better than the current literature. |
Year | Venue | Field |
|---|---|---|
2015 | CoRR | Pattern recognition,Vector bundle,Vector field,Computer science,Invariant (mathematics),Artificial intelligence,Statistical classification,Manifold,Machine learning,Geodesic,Tangent space,Covariance |
DocType | Volume | Citations |
Journal | abs/1503.06699 | 8 |
PageRank | References | Authors |
0.67 | 18 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhengwu Zhang | 1 | 46 | 8.35 |
| Jing-yong Su | 2 | 156 | 10.93 |
| Eric Klassen | 3 | 801 | 41.13 |
| huiling le | 4 | 36 | 3.45 |
| Anuj Srivastava | 5 | 2853 | 199.47 |