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.

Since the soft shadow achieved by the existing shadow generation algorithms of Augmented Reality (AR) is unrealistic, the authors proposed a shadow generation algorithm using adaptive sampling and background fusion. First, the authors computed shadow spatial location distribution of virtual objects by using planar shadow algorithm which took occlusion into account. Then, to improve the procedure of soft shadow generation in swell and erode algorithm, an adaptive sampling method which got illuminant union according to shape types was presented. Finally, since shadow color gotten by gray image method was limited to single channel, the authors presented a method based on multi-channel and background fusion. The experimental results show that in the proposed algorithm the color of soft shadow is more reasonable and the method of soft shadow rendering is more effective. Consequently, the presented algorithm improves the realism of soft shadow.