Temporal dynamics is an open issue for modeling human body gestures. A solution is resorting to the generative models, such as the hidden Markov model (HMM). Nevertheless, most of the work assumes fixed anchors for each hidden state, which make it hard to describe the explicit temporal structure of gestures. Based on the observation that a gesture is a time series with distinctly defined phases, we propose a new formulation to build temporal compositions of gestures by the low-rank matrix decomposition. The only assumption is that the gesture’s “hold” phases with static poses are linearly correlated among each other. As such, a gesture sequence could be segmented into temporal states with semantically meaningful and discriminative concepts. Furthermore, different to traditional HMMs which tend to use specific distance metric for clustering and ignore the temporal contextual information when estimating the emission probability, we utilize the long short-term memory to learn probability distributions over states of HMM. The proposed method is validated on multiple challenging datasets. Experiments demonstrate that our approach can effectively work on a wide range of gestures, and achieve state-of-the-art performance.
Liu Xin, Shi Henglin, Hong Xiaopeng, Chen Haoyu, Tao Dacheng, Zhao Guoying
A1 Journal article – refereed
Place of publication:
X. Liu, H. Shi, X. Hong, H. Chen, D. Tao and G. Zhao, “3D Skeletal Gesture Recognition via Hidden States Exploration,” in IEEE Transactions on Image Processing, vol. 29, pp. 4583-4597, 2020, https://doi.org/10.1109/TIP.2020.2974061
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