• 제목/요약/키워드: RGB-D videos

검색결과 2건 처리시간 0.014초

Spatial-temporal texture features for 3D human activity recognition using laser-based RGB-D videos

  • Ming, Yue;Wang, Guangchao;Hong, Xiaopeng
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제11권3호
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    • pp.1595-1613
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    • 2017
  • The IR camera and laser-based IR projector provide an effective solution for real-time collection of moving targets in RGB-D videos. Different from the traditional RGB videos, the captured depth videos are not affected by the illumination variation. In this paper, we propose a novel feature extraction framework to describe human activities based on the above optical video capturing method, namely spatial-temporal texture features for 3D human activity recognition. Spatial-temporal texture feature with depth information is insensitive to illumination and occlusions, and efficient for fine-motion description. The framework of our proposed algorithm begins with video acquisition based on laser projection, video preprocessing with visual background extraction and obtains spatial-temporal key images. Then, the texture features encoded from key images are used to generate discriminative features for human activity information. The experimental results based on the different databases and practical scenarios demonstrate the effectiveness of our proposed algorithm for the large-scale data sets.

물체 탐지기와 위치 사전 확률 지도를 이용한 효율적인 3차원 장면 레이블링 (Efficient 3D Scene Labeling using Object Detectors & Location Prior Maps)

  • 김주희;김인철
    • 제어로봇시스템학회논문지
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    • 제21권11호
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    • pp.996-1002
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    • 2015
  • In this paper, we present an effective system for the 3D scene labeling of objects from RGB-D videos. Our system uses a Markov Random Field (MRF) over a voxel representation of the 3D scene. In order to estimate the correct label of each voxel, the probabilistic graphical model integrates both scores from sliding window-based object detectors and also from object location prior maps. Both the object detectors and the location prior maps are pre-trained from manually labeled RGB-D images. Additionally, the model integrates the scores from considering the geometric constraints between adjacent voxels in the label estimation. We show excellent experimental results for the RGB-D Scenes Dataset built by the University of Washington, in which each indoor scene contains tabletop objects.