{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T13:46:15Z","timestamp":1760708775558,"version":"build-2065373602"},"reference-count":17,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2015,1,8]],"date-time":"2015-01-08T00:00:00Z","timestamp":1420675200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper, a three-dimensional (3D) object moving direction and velocity estimation method is presented using a dual off-axis color-filtered aperture (DCA)-based computational camera. Conventional object tracking methods provided only two-dimensional (2D) states of an object in the image for the target representation. The proposed method estimates depth information in the object region from a single DCA camera that transforms 2D spatial information into 3D model parameters of the object. We also present a calibration method of the DCA camera to estimate the entire set of camera parameters for a practical implementation. Experimental results show that the proposed DCA-based color and depth (RGB-D) camera can calculate the 3D object moving direction and velocity of a randomly moving object in a single-camera framework.<\/jats:p>","DOI":"10.3390\/s150100995","type":"journal-article","created":{"date-parts":[[2015,1,8]],"date-time":"2015-01-08T11:29:32Z","timestamp":1420716572000},"page":"995-1007","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Three-Dimensional Object Motion and Velocity Estimation Using a Single Computational RGB-D Camera"],"prefix":"10.3390","volume":"15","author":[{"given":"Seungwon","family":"Lee","sequence":"first","affiliation":[{"name":"Image Processing and Intelligent Systems Laboratory Graduate School of Advanced Imaging Science, Multimedia, and Film Chung-Ang University, Seoul 156-756, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kyungwon","family":"Jeong","sequence":"additional","affiliation":[{"name":"Image Processing and Intelligent Systems Laboratory Graduate School of Advanced Imaging Science, Multimedia, and Film Chung-Ang University, Seoul 156-756, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinho","family":"Park","sequence":"additional","affiliation":[{"name":"Image Processing and Intelligent Systems Laboratory Graduate School of Advanced Imaging Science, Multimedia, and Film Chung-Ang University, Seoul 156-756, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8593-7155","authenticated-orcid":false,"given":"Joonki","family":"Paik","sequence":"additional","affiliation":[{"name":"Image Processing and Intelligent Systems Laboratory Graduate School of Advanced Imaging Science, Multimedia, and Film Chung-Ang University, Seoul 156-756, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2015,1,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"13679","DOI":"10.3390\/s140813679","article-title":"Thermal tracking of sports players","volume":"14","author":"Gade","year":"2014","journal-title":"Sensors"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"11245","DOI":"10.3390\/s140611245","article-title":"Robust pedestrian tracking and recognition from FLIR video a unified approach via sparse coding","volume":"14","author":"Li","year":"2014","journal-title":"Sensors"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"11362","DOI":"10.3390\/s140711362","article-title":"Directional joint bilateral filter for depth images","volume":"14","author":"Le","year":"2014","journal-title":"Sensors"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"8895","DOI":"10.3390\/s130708895","article-title":"Background subtraction based on color and depth using active sensors","volume":"13","author":"Diaz","year":"2013","journal-title":"Sensors"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1961","DOI":"10.3390\/s140201961","article-title":"Foreground segmentation in depth imagery using depth and spatial dynamic models for video surveillance applications","volume":"14","author":"Blanco","year":"2014","journal-title":"Sensors"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"5280","DOI":"10.3390\/s100505280","article-title":"A bayesian framework for human body pose tracking from depth image sequences","volume":"10","author":"Zhu","year":"2010","journal-title":"Sensors"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1498","DOI":"10.1109\/21.44067","article-title":"Structure from stereo-a review","volume":"19","author":"Dhond","year":"1989","journal-title":"IEEE Trans. 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