Abstract
Human gesture has the characteristics of intuitive, natural and informative, and is one of the most commonly used interaction methods. However, in most gesture interaction researches, the hand to be detected is facing detection camera, and experiment environment is ideal. There is no guarantee that these methods can achieve perfect detection results in practical applications. Therefore, gesture interaction in complex environments has high research and application value. This paper discusses hand segmentation and gesture contour extraction methods in complex environments and specific applications: First, according to the characteristics of depth map, an adaptive weighted median filtering method is selected to process depth data; then use depth information to construct background model which can reduce interference of noise and light changes, and combine RGB information and depth threshold segmentation to complete hand segmentation; finally, region grow method is used to extract precise gesture contour. This paper verifies the proposed method by using vehicle environment material, and obtains satisfactory segmentation and contour extraction results.
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Wang, J., Wang, Z., Fu, S., Huang, D. (2021). Research on Hand Detection in Complex Scenes Based on RGB-D Sensor. In: Kurosu, M. (eds) Human-Computer Interaction. Interaction Techniques and Novel Applications. HCII 2021. Lecture Notes in Computer Science(), vol 12763. Springer, Cham. https://doi.org/10.1007/978-3-030-78465-2_12
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DOI: https://doi.org/10.1007/978-3-030-78465-2_12
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