In order to improve the recognition ability for online behavior continuous sequences and enhance the stability of behavior recognition model, a novel online behavior recognition method based on Probabilistic Latent-Dynamic Conditional Random Field (PLDCRF) from surveillance video was proposed. Firstly, the Space-Time Interest Point (STIP) was used to extract behavior features. Then, the PLDCRF model was applied to identify the activity state of indoor human body. The proposed PLDCRF model incorporates the hidden state variables and can construct the substructure of gesture sequences. It can select the dynamic features of gesture and mark the unsegmented sequences directly. At the same time, it can also mark the conversion process between behaviors correctly to improve the effect of behavior recognition greatly. Compared with Hidden Conditional Random Field (HCRF), Latent-Dynamic Conditional Random Field (LDCRF) and Latent-Dynamic Conditional Neural Field (LDCNF), the recognition rate comparison results of 10 different behaviors show that, the proposed PLDCRF model has a stronger recognition ability for continuous behavior sequences and better stability.

Aiming at the problem that the sub-health state of subway door is difficult to identify, a sub-health state identification method based on time series data mining was proposed. First of all, the angle, speed and current data of the subway door motor were discretized by combining multi-scale sliding window method and Extension of Symbolic Aggregate approXimation (ESAX) algorithm. And then, the features were obtained by calculating the distances among the templates under the normal state of the subway door, in which the Principal Component Analysis (PCA) was adopted to reduce feature dimension. Finally, combining with basic features, a hierarchical pattern recognition model was proposed to identify the sub-health state from coarse to fine. The real test data of subway door were taken as examples to verify the effectiveness of the proposed method. The experimental results show that the proposed method can recognize sub-health state effectively, and its recognition rate can reach 99%.

The neural activity signal of interest is often influenced by structural noise and random noise in functional Magnetic Resonance Imaging (fMRI) data. In order to eliminate noise effects in the analysis of activate voxels, the time series of voxels preprocessed by Statistical Parametric Mapping (SPM) were transformed by Activelets wavelet. After getting scale coefficient and detail coefficient, the two kinds of noise denoised were eliminated separately according to their corresponding characteristics. Firstly, the Independent Component Analysis (ICA) was used to identify and eliminate the structural noise sources. Secondly, an improved algorithm for spatial correlation was presented on the detail coefficient. In particular, in the improved algorithm, the voxel similarity in the neighborhood was used to determine whether the detail coefficient reflected the noise or the neural activity. Experimental results show that the processing of data effectively eliminate the effect of noise; specifically, the frame displacement decreased by 1.5mm and the percentage of spikes decreased by 2%; in addition, the false activation regions are obviously restrained in the spatial map got by denoised signals.