Sparse motion bases selection for human motion denoising

Jun Xiao, Yinfu Feng, Mingming Ji, Xiaosong Yang, Jian J. Zhang, Yueting Zhuang

Abstract: Highlights • A fine-grained pose representation model is proposed to boost the performance. • We present a data-driven based motion denoising method by solving ℓ1-minimization problems. • The proposed model selects the most correlated motion bases for motion reconstruction. • Our method does not need to choose the training data and can be implemented in parallel mode. Abstract Human motion denoising is an indispensable step of data preprocessing for many motion data based applications. In this paper, we propose a data-driven based human motion denoising method that sparsely selects the most correlated subset of motion bases for clean motion reconstruction. Meanwhile, it takes the statistic property of two common noises, i.e., Gaussian noise and outliers, into account in deriving the objective functions. In particular, our method firstly divides each human pose into five partitions termed as poselets to gain a much fine-grained pose representation. Then, these poselets are reorganized into multiple overlapped poselet groups using a lagged window moving across the entire motion sequence to preserve the embedded spatial–temporal motion patterns. Afterward, five compacted and representative motion dictionaries are constructed in parallel by means of fast K-SVD in the training phase; they are used to remove the noise and outliers from noisy motion sequences in the testing phase by solving ℓ1-minimization problems. Extensive experiments show that our method outperforms its competitors. More importantly, compared with other data-driven based method, our method does not need to specifically choose the training data, it can be more easily applied to real-world applications.