Abstract: 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.

Key words: video surveillance, online behavior recognition, Space-Time Interest Point (STIP), Probabilistic Latent-Dynamic Conditional Random Field (PLDCRF)

摘要： 针对在线行为连续序列的识别问题以及行为识别模型的稳定性问题，提出一种监控视频中基于概率潜动态条件随机场（PLDCRF）的在线行为识别方法。首先，应用时空兴趣点（STIP）对行为特征进行提取；再利用PLDCRF模型识别室内人体的活动状态。PLDCRF模型融合了隐含状态变量，能够构建姿态序列子结构，可以选取姿态之间的动态特征，并且直接标记出未分割序列；同时也可以正确地标记出行为间的转换过程，从而明显改善了行为识别的效果。隐含条件随机场（HCRF）、潜动态条件随机场（LDCRF）、潜动态条件神经场（LDCNF）以及PLDCRF模型对10种不同动作的识别率比较结果表明，所提PLDCRF模型对连续的行为序列的综合识别能力更强，并且有更好的稳定性。

关键词: 视频监控, 在线行为识别, 时空兴趣点, 概率潜动态条件随机场