• Title/Summary/Keyword: multiply-cameras

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Object Tracking System Using Kalman Filter (칼만 필터를 이용한 물체 추적 시스템)

  • Xu, Yanan;Ban, Tae-Hak;Yuk, Jung-Soo;Park, Dong-Won;Jung, Hoe-kyung
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2013.10a
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    • pp.1015-1017
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    • 2013
  • Object tracking, in general, is a challenging problem. Difficulties in tracking objects can arise due to abrupt object motion, changing appearance patterns of both the object and the scene, non-rigid object structures, object-to-object and object-to-scene occlusions, and camera motion. Tracking is usually performed in the context of higher-level applications that require the location or the shape of the object in every frame. This paper describes an object tracking system based on active vision with two cameras, into algorithm of single camera tracking system an object active visual tracking and object locked system based on Extend Kalman Filter (EKF) is introduced, by analyzing data from which the next running state of the object can be figured out and after the tracking is performed at each of the cameras, the individual tracks are to be fused (combined) to obtain the final system object track.

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3D reconstruction method without projective distortion from un-calibrated images (비교정 영상으로부터 왜곡을 제거한 3 차원 재구성방법)

  • Kim, Hyung-Ryul;Kim, Ho-Cul;Oh, Jang-Suk;Ku, Ja-Min;Kim, Min-Gi
    • Proceedings of the IEEK Conference
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    • 2005.11a
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    • pp.391-394
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    • 2005
  • In this paper, we present an approach that is able to reconstruct 3 dimensional metric models from un-calibrated images acquired by a freely moved camera system. If nothing is known of the calibration of either camera, nor the arrangement of one camera which respect to the other, then the projective reconstruction will have projective distortion which expressed by an arbitrary projective transformation. The distortion on the reconstruction is removed from projection to metric through self-calibration. The self-calibration requires no information about the camera matrices, or information about the scene geometry. Self-calibration is the process of determining internal camera parameters directly from multiply un-calibrated images. Self-calibration avoids the onerous task of calibrating cameras which needs to use special calibration objects. The root of the method is setting a uniquely fixed conic(absolute quadric) in 3D space. And it can make possible to figure out some way from the images. Once absolute quadric is identified, the metric geometry can be computed. We compared reconstruction image from calibrated images with the result by self-calibration method.

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