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- W4287964657 abstract "Gait-based human recognition is a promising biometric modality for automatic visual surveillance. Over the past three decades, human gait has been the subject of permanent research based on video sequences of varying resolution, scale and duration. The range of distinct features expends when various physical devices are used to capture data about gait cycle. However, the use of a single camera remains the main technical solution in many surveillance and recognition tasks. This study aims to improve the extraction of human gait patterns using novel approach to motion interpolation based on optical flow and deep learning techniques. Interpolated optical flow maps provide more information than the binary silhouette technique does. This allows to get better recognition results. We obtained good experimental results for silhouette representations, especially for low-frequency test videos. The proposed technique is useful for constructing a gait model of individual at the training stage." @default.
- W4287964657 created "2022-07-26" @default.
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- W4287964657 date "2022-01-01" @default.
- W4287964657 modified "2023-09-25" @default.
- W4287964657 title "Accurate Extraction of Human Gait Patterns Using Motion Interpolation" @default.
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- W4287964657 doi "https://doi.org/10.1007/978-981-19-3444-5_18" @default.
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