School of Computer Science and Technology, Xidian University, Xi’an, P.R.China

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Abstract—This paper presents a novel classification method for interactive activities, which is represented by ℜ transform descriptor and classified by Hidden Markov Models (HMMs). It solves the problem that trades off activity recognition rate and computational complexity, rather than highlight the former exclusively. We extract binary silhouette images after the background model is created. Then the low-level features are described by ℜ transform and principal vectors are determined by Principal Component Analysis (PCA). We utilize HMMs to train and classify video sequences, and demonstrate the usability with many sequences. Compared with others, our method is applicable to intelligent surveillance, as its advantage of ℜ transform descriptor lying in robustness, computational complexity, geometric invariance and classification performance, and HMM in medium computational cost. So the video surveillance based on these is practicable in (but not limited to) many scenarios where the background is known.

Index Terms—feature extraction, activity classification, ℜ transform descriptor, Hidden Markov Models (HMMs), video surveillance.

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Cite: Hao Zhang, Zhijing Liu, Haiyong Zhao, "Automated Classification of Two Persons’ Interactive Activities," Journal of Software vol. 5, no. 8, pp. 810-817, 2010.