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Sequential Markov random fields for human body parts tracking

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Abstract

This paper presents a novel human body part tracker called sequential Markov random fields (SMRFs), which can be used to extract spatiotemporal features in human action recognition. Given a video sequence of human action in the monocular settings, SMRF can effectively detect the key spatiotemporal feature points on human body parts. We also develop efficient learning algorithms for the SMRF tracker using relaxation labeling (RL). Our results show that the SMRF tracker performs better than some state-of-the-art trackers for human action recognition.

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Notes

  1. We denote \(p\left(y_i^t,y_{i^{\prime}}^t\big|f_i^t=L_j^t,f_{i^{\prime}}^t=L_{j'}^t\right)\) as \(p\left (y_i^t,y_{i^{\prime }}^t\big |L_j^t,L_{j^{\prime }}^t\right )\)when we consider the structure of a pose.

  2. We denote \(p\left (y_i^t|f_i^t=\hat {x}_j^t\right )p\left (y_i^t, y_i^{t-1}| f_i^t=x_j^t,f_i^{t-1}=x_j^{t-1}\right )\)as \(p\left (y_i^t|\hat {x}_j^t\right )p\left (y_i^t, y_i^{t-1}| x_j^t,x_j^{t-1}\right )\)when we consider the dynamic motion.

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Acknowledgments

This work is supported by a GRF grant from RGC UGC Hong Kong (GRF Project No.9041574) and a grant from City University of Hong Kong (Project No. 7008026). It is partly supported by by NSFC (Grant No. 61003154, 61373092 and 61033013), Natural Science Foundation of the Jiangsu Higher Education Institutions of China (Grant No. 12KJA520004) and Innovative Research Teamin Soochow University (Grant No. SDT2012B02).

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  1. School of Creative Media, City University of Hong Kong, Tat Chee Avenue 83, Kowloon Tong, Hong Kong

    Xiao-Qin Cao & Zhi-Qiang Liu

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  1. Xiao-Qin Cao
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  2. Zhi-Qiang Liu
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Correspondence to Xiao-Qin Cao.

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Cao, XQ., Liu, ZQ. Sequential Markov random fields for human body parts tracking. Multimed Tools Appl 74, 6671–6690 (2015). https://doi.org/10.1007/s11042-014-1924-3

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