![\"\"](\"http:\/\/bioeng.nus.edu.sg\/mm\/wp-content\/uploads\/2013\/12\/FusionDiagram-1jec84j.png\" "\\"FusionDiagram\\"")
<\/a>Fig. 1 Block diagram of hierarchical fusion algorithm.<\/p><\/div>
\nThe low-level fusion is to improve the speed and robustness of marker feature extraction before triangulating the tool tip position in IR and RGB camera pair. The IR and RGB camera are modeled as pin-hole cameras. \u00a0The depth data of the tool can be used as a priori for marker detection. The working area of the tracking tool is supposed to be limited in a reasonable volume v(x, y, z) that can be used to refine the search area for feature extraction, which could reduce the computational cost for real-time applications.
\nThe high-level fusion is to reach a highly accurate tracking result by fusing two measurements. We employ the covariance intersection (CI) algorithm to estimate a new tracking result with less covariance.<\/p>\n
Experiments<\/h2>\n
To demonstrate the proposed algorithm, we designed a hybrid marker-based tracking tool (Fig. 2) that incorporates the cross-based feature in visible modality and retro-reflective marker based feature in infra-red modality to get a fused tracking of the customized tool tip. To evaluate the performance of the proposed method, we employ two Kinects to build the experimental setup. Fig. 3 shows the prototype of multi-sensor fusion tracker for the experiment, which\u00a0indicates that the CI-based fusion approaches obviously tend to be better than the separate IR tracker or RGB tracker.\u00a0 The mean error and deviation of the fusion algorithm are all improved. Fig. 3 Dual Kinect tracking system<\/p><\/div><\/p>\n Staffs: Wei LIU, Shuang SONG, Andy Lim [1] Ren, H.; LIU, W. & LIM, A. Marker-Based Instrument Tracking Using Dual Kinect Sensors for Navigated Surgery IEEE Transactions on Automation Science and Engineering, 2013 Andy Lim: Marker-Based Surgical Tracking With Multiple Modalities Using Microsoft Kinect [1] H. Ren, D. Rank, M. Merdes, J. Stallkamp, and P. Kazanzides, \u201cMulti-sensor data fusion in an integrated tracking system for endoscopic surgery,\u201d IEEE Transactions on Information Technology in Biomedicine, vol. 16, no. 1, pp. 106 \u2013 111, 2012. Project Goals: The objective of this research is to incorporate multiple sensors at broad spectrum, including stereo infrared (IR) cameras, color (or RGB) cameras and depth sensors to perceive the surgical environment. Features extracted from each modality can contribute to the cognition of complex surgical environment or procedures. Additionally, their\u2026 Continue reading<\/a><\/p>\n","protected":false},"author":1,"featured_media":1337,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"ngg_post_thumbnail":0,"footnotes":""},"categories":[44,46,9],"tags":[74,100],"class_list":["post-2683","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-navigation","category-positioning","category-projects","tag-kinect","tag-tracking"],"_links":{"self":[{"href":"http:\/\/www.labren.org\/mm\/wp-json\/wp\/v2\/posts\/2683","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/www.labren.org\/mm\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.labren.org\/mm\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.labren.org\/mm\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/www.labren.org\/mm\/wp-json\/wp\/v2\/comments?post=2683"}],"version-history":[{"count":0,"href":"http:\/\/www.labren.org\/mm\/wp-json\/wp\/v2\/posts\/2683\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"http:\/\/www.labren.org\/mm\/wp-json\/wp\/v2\/media\/1337"}],"wp:attachment":[{"href":"http:\/\/www.labren.org\/mm\/wp-json\/wp\/v2\/media?parent=2683"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.labren.org\/mm\/wp-json\/wp\/v2\/categories?post=2683"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.labren.org\/mm\/wp-json\/wp\/v2\/tags?post=2683"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
\n<\/a>
\n<\/a>People Involved<\/h2>\n
\nAdvisor: Dr. Hongliang Ren
\nCollaborator: Wei ZHANG<\/p>\nPublications<\/h2>\n
\n[2] Liu, W.; Ren, H.; Zhang, W. & Song, S. Cognitive Tracking of Surgical Instruments Based on Stereo Vision and Depth Sensing, ROBIO 2013, IEEE International Conference on Robotics and Biomimetics, 2013<\/p>\nRelated FYP Project<\/h2>\n
\n<\/a><\/p>\nReferences<\/h2>\n
\n[2] W. Liu, C. Hu, Q. He, and M.-H. Meng, \u201cA three-dimensional visual localization system based on four inexpensive video cameras,\u201d in Information and Automation (ICIA), 2010 IEEE International Conference on. IEEE, 2010, pp. 1065\u20131070.
\n[3] F. Faion, S. Friedberger, A. Zea, and U. D. Hanebeck, \u201cIntelligent sensor-scheduling for multi-kinect-tracking,\u201d in Intelligent Robots and Systems (IROS), 2012 IEEE\/RSJ International Conference on. IEEE, 2012, pp. 3993\u20133999.<\/p>\n","protected":false},"excerpt":{"rendered":"