Human activity recognition based on HMM by improved PSO and event probability sequence  

作　　者：Hanju Li Yang Yi Xiaoxing Li Zixin Guo 

机构地区：[1]Department of Computer Science,Sun Yat-sen University [2]Information Centre,Dongguan Power Supply Bureau,Guangdong Power Grid Co [3]Xinhua College,Sun Yat-sen University

出　　处：《Journal of Systems Engineering and Electronics》2013年第3期545-554,共10页系统工程与电子技术（英文版）

基　　金：supported by the National Natural Science Foundation of China(60573159);the Guangdong High Technique Project(201100000514)

摘　　要：This paper proposes a hybrid approach for recognizing human activities from trajectories. First, an improved hidden Markov model （HMM） parameter learning algorithm, HMM-PSO, is proposed, which achieves a better balance between the global and local exploitation by the nonlinear update strategy and repulsion operation. Then, the event probability sequence （EPS） which consists of a series of events is computed to describe the unique characteristic of human activities. The anatysis on EPS indicates that it is robust to the changes in viewing direction and contributes to improving the recognition rate. Finally, the effectiveness of the proposed approach is evaluated by data experiments on current popular datasets.This paper proposes a hybrid approach for recognizing human activities from trajectories. First, an improved hidden Markov model （HMM） parameter learning algorithm, HMM-PSO, is proposed, which achieves a better balance between the global and local exploitation by the nonlinear update strategy and repulsion operation. Then, the event probability sequence （EPS） which consists of a series of events is computed to describe the unique characteristic of human activities. The anatysis on EPS indicates that it is robust to the changes in viewing direction and contributes to improving the recognition rate. Finally, the effectiveness of the proposed approach is evaluated by data experiments on current popular datasets.

关 键 词：human activity recognition hidden Markov model （HMM） event probability sequence （EPS） particle swarm optimization （PSO）. 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]