Title :
Human behavior recognition based on fractal conditional random field
Author :
Zhuowen Lv ; Kejun Wang
Author_Institution :
Coll. of Autom., Harbin Eng. Univ., Harbin, China
Abstract :
In order to meet the demand of visual behavior recognition, we introduce Fractal Conditional Random Field (FCRF) model. FCRF model has improved Latent-Dynamic Conditional Random Field (LDCRF), and proposed the concept of fractal labels that define the integrity and directionality of human behavior. FCRF model overcomes real-time issues of the Hidden Conditional Random Field (HCRF) and the problem of label bias when the behavior transform. The experimental results show that the algorithm proposed in this paper has better recognition performance than Conditional Random Field (CRF), HCRF and LDCRF.
Keywords :
fractals; gesture recognition; statistical analysis; FCRF model; HCRF; LDCRF; fractal conditional random field model; fractal labels; hidden conditional random field; human behavior directionality; human behavior integrity; human behavior recognition; label bias problem; latent-dynamic conditional random field; visual behavior recognition; Adaptation models; Fractals; Hidden Markov models; Mathematical model; Testing; Training; Video sequences; CRF; FCRF; HCRF; LDCRF; behavior recognition;
Conference_Titel :
Control and Decision Conference (CCDC), 2013 25th Chinese
Conference_Location :
Guiyang
Print_ISBN :
978-1-4673-5533-9
DOI :
10.1109/CCDC.2013.6561166