Adaptive Self-Occlusion Behavior Recognition Based on pLSA

Abstract

Human action recognition is an important area of human action recognition research. Focusing on the problem of self-occlusion in the field of human action recognition, a new adaptive occlusion state behavior recognition approach was presented based on Markov random field and probabilistic Latent Semantic Analysis (pLSA). Firstly, the Markov random field was used to represent the occlusion relationship between human body parts in terms an occlusion state variable by phase space obtained. Then, we proposed a hierarchical area variety model. Finally, we use the topic model of pLSA to recognize the human behavior. Experiments were performed on the KTH, Weizmann, and Humaneva dataset to test and evaluate the proposed method. The compared experiment results showed that what the proposed method can achieve was more effective than the compared methods.

The Approach for Action Recognition Based on the Reconstructed Phase Spaces

“…The human body [ 2 ] is divided into 15 key points, which are named 15 key joint points for representing the human body structure (torso, pelvis, left upper leg, left lower leg, left foot, right upper leg, right lower leg, right foot, left upper arm, left lower arm, left hand, right upper arm, right lower arm, right hand, and head) [ 17 ], which the 15 joints trajectory represents the human body behavior (blue dot represents pelvis, which is the origin of coordinate). Another consideration was that these joints were relatively easy to automatically detect and track in real videos, as opposed to the inner body joints which were more difficult to track.…”