Logistic dynamic texture model for human activity and gait recognition

In this paper, a logistic dynamic texture model (LDT) is proposed to characterize binary image sequences. Dynamic texture model (DT) is one of the most efficient and successful methods in modeling dynamic sequences. It learns the parameters through a closed-form solution and commonly uses principal component analysis (PCA) to obtain the observation function. PCA assumes a Gaussian distribution over a set of observations. However, the binary image sequences subject to Bernoulli distribution. The LDT introduces logistic PCA to learn the observation function. The proposed model is capable of describing the binary image sequences accurately by processing the pixels of 1 and 0 separately. The model is demonstrated by image reconstructing and activity/gait recognition experiments. Experimental results illustrate the effectiveness of our model.