3D skeleton based human representation for gesture recognition has increasingly attracted attention due to its invariance to camera view and environment dynamics. Existing methods typically utilize absolute coordinate to present human motion features. However, gestures are independent of the performer’s locations, and the features should be invariant to the body size of performer. Moreover, temporal dynamics can significantly distort the distance metric when comparing and identifying gestures. In this paper, we represent each skeleton as a point in the product space of special orthogonal group SO3, which explicitly models the 3D geometric relationships between body parts. Then, a gesture skeletal sequence can be characterized by a trajectory on a Riemannian manifold. Next, we generalize the transported square-root vector field to obtain a re-parametrization invariant metric on the product space of SO(3), therefore, the goal of comparing trajectories in a time-warping invariant manner is realized. Furthermore, we present a sparse coding of skeletal trajectories by explicitly considering the labeling information with each atoms to enforce the discriminant validity of dictionary. Experimental results demonstrate that proposed method has achieved state-of-the-art performance on three challenging benchmarks for gesture recognition.
Liu Xin, Zhao Guoying
A4 Article in conference proceedings
Place of publication:
MultiMedia Modeling. MMM 2019
Liu X., Zhao G. (2019) 3D Skeletal Gesture Recognition via Sparse Coding of Time-Warping Invariant Riemannian Trajectories. In: Kompatsiaris I., Huet B., Mezaris V., Gurrin C., Cheng WH., Vrochidis S. (eds) MultiMedia Modeling. MMM 2019. Lecture Notes in Computer Science, vol 11295. Springer, Cham
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