A non-linear mapping representing human action recognition under missing modality problem in video data

doi:10.1016/j.measurement.2021.110123

A non-linear mapping representing human action recognition under missing modality problem in video data

2021-12-01

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DOI：10.1016/j.measurement.2021.110123

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Human action recognition by using standard video files is a well-studied problem in the literature. In this study, we assume to have access to single modality standard data of some actions (training data). Based on this data, we aim at identifying the actions that are present in a target modality video data without any explicit source- target relationship information. In this case, the training and test phases of the recognition task are based on different imaging modalities. Our goal in this paper is to introduce a mapping (a nonlinear operator) on both modalities such that the outcome shares some common features. These common features were then used to recognize the actions in each domain. Simulation results on MSRDailyActivity3D, MSRActionPairs, UTKinectAction3D, and SBU Kinect interaction datasets showed that the introduced method outperforms state-of-the art methods with a success rate margin of 15% on average.

Keywords:

Missing modality

Human action recognition

RGB-D data

A non-linear mapping representing human action recognition under missing modality problem in video data

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