• 제목/요약/키워드: Locality sensitive hashing

검색결과 12건 처리시간 0.015초

모바일 환경 Homography를 이용한 특징점 기반 다중 객체 추적 (Multi-Object Tracking Based on Keypoints Using Homography in Mobile Environments)

  • 한우리;김영섭;이용환
    • 반도체디스플레이기술학회지
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    • 제14권3호
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    • pp.67-72
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    • 2015
  • This paper proposes an object tracking system based on keypoints using homography in mobile environments. The proposed system is based on markerless tracking, and there are four modules which are recognition, tracking, detecting and learning module. Recognition module detects and identifies an object to be matched on current frame correspond to the database using LSH through SURF, and then this module generates a standard object information. Tracking module tracks an object using homography information that generate by being matched on the learned object keypoints to the current object keypoints. Then update the window included the object for defining object's pose. Detecting module finds out the object based on having the best possible knowledge available among the learned objects information, when the system fails to track. The experimental results show that the proposed system is able to recognize and track objects with updating object's pose for the use of mobile platform.

모바일 환경 신뢰도 평가 학습에 의한 다중 객체 추적 (Multi-Object Tracking based on Reliability Assessment of Learning in Mobile Environment)

  • 한우리;김영섭;이용환
    • 반도체디스플레이기술학회지
    • /
    • 제14권3호
    • /
    • pp.73-77
    • /
    • 2015
  • This paper proposes an object tracking system according to reliability assessment of learning in mobile environments. The proposed system is based on markerless tracking, and there are four modules which are recognition, tracking, detecting and learning module. Recognition module detects and identifies an object to be matched on current frame correspond to the database using LSH through SURF, and then this module generates a standard object information that has the best reliability of learning. The standard object information is used for evaluating and learning the object that is successful tracking in tracking module. Detecting module finds out the object based on having the best possible knowledge available among the learned objects information, when the system fails to track. The experimental results show that the proposed system is able to recognize and track the reliable objects with reliability assessment of learning for the use of mobile platform.