Continuous human action recognition using depth-MHI-HOG and a spotter model.
In this paper, we propose a new method for spotting and recognizing continuous human actions using a vision sensor. The method is comprised of depth-MHI-HOG (DMH), action modeling, action spotting, and recognition. First, to effectively separate the foreground from background, we propose a method called DMH. It includes a standard structure for segmenting images and extracting features by using depth information, MHI, and HOG. Second, action modeling is performed to model various actions using extracted features. The modeling of actions is performed by creating sequences of actions through k-means clustering; these sequences constitute HMM input. Third, a method of action spotting is proposed to filter meaningless actions from continuous actions and to identify precise start and end points of actions. By employing the spotter model, the proposed method improves action recognition performance. Finally, the proposed method recognizes actions based on start and end points. We evaluate recognition performance by employing the proposed method to obtain and compare probabilities by applying input sequences in action models and the spotter model. Through various experiments, we demonstrate that the proposed method is efficient for recognizing continuous human actions in real environments.
School of Electrical and Electronic Engineering, Yonsei University, 134 Shinchon-Dong, Seodaemun-Gu, Seoul 120-749, Korea. hmeum@yonsei.ac.kr.
Hyukmin
H
Eum
Department of Electrical Engineering, Suwon Science College, Hwaseong 445-742, Korea. cyyoon@ssc.ac.kr.
Changyong
C
Yoon
Department of Electrical, Electronic and Control Engineering, Hankyong National University, Anseong 456-749, Korea. lhjin@hknu.ac.kr.
Heejin
H
Lee
School of Electrical and Electronic Engineering, Yonsei University, 134 Shinchon-Dong, Seodaemun-Gu, Seoul 120-749, Korea. mignpark@yonsei.ac.kr.
Mignon
M
Park
D001185:Artificial Intelligence; D006802:Human Activities; D006801:Humans; D008390:Markov Chains; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated; D014741:Video Recording