{"id":248366,"date":"2004-05-05T00:38:35","date_gmt":"2004-05-05T07:38:35","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=248366"},"modified":"2018-10-16T20:16:33","modified_gmt":"2018-10-17T03:16:33","slug":"towards-calibrating-pan-tilt-zoom-camera-network","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/towards-calibrating-pan-tilt-zoom-camera-network\/","title":{"rendered":"Towards calibrating a pan-tilt-zoom camera network"},"content":{"rendered":"

In this paper we discuss the problem of recovering the calibration of a network of pan-tilt-zoom cameras. The intrinsic parameters of each camera over its full range of zoom settings are estimated through a two step procedure. We first determine the intrinsic parameters at the camera\u2019s lowest zoom setting very accurately by capturing an extended panorama. Our model includes two parameters of radial distortion. The camera intrinsics and radial distortion parameters are then determined at discrete steps in a monotonically increasing zoom sequence that spans the full zoom range of the cameras. Both steps are fully automatic and do not assume any knowledge of the scene structure. We validate our approach by calibrating two different types of pan tilt zoom cameras placed in an outdoor environment. We also show the high-resolution panoramic mosaics built from the images captured during this process. We present an approach for accurate computation of the epipolar geometry based on the full panorama instead of individual image pairs. Finally, we briefly discuss how this can be used to compute the extrinsics for all the cameras and how our approach can be used in the context of active camera networks.<\/p>\n","protected":false},"excerpt":{"rendered":"

In this paper we discuss the problem of recovering the calibration of a network of pan-tilt-zoom cameras. The intrinsic parameters of each camera over its full range of zoom settings are estimated through a two step procedure. We first determine the intrinsic parameters at the camera\u2019s lowest zoom setting very accurately by capturing an extended […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13562],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-248366","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-computer-vision","msr-locale-en_us"],"msr_publishername":"","msr_edition":"ECCV workshop on Omnidirectional Vision and Camera 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