• Title/Summary/Keyword: Probabilistic Multi-Hypothesis Tracking method

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A Study of Automatic Multi-Target Detection and Tracking Algorithm using Highest Probability Data Association in a Cluttered Environment (클러터가 존재하는 환경에서의 HPDA를 이용한 다중 표적 자동 탐지 및 추적 알고리듬 연구)

  • Kim, Da-Soul;Song, Taek-Lyul
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.56 no.10
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    • pp.1826-1835
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    • 2007
  • In this paper, we present a new approach for automatic detection and tracking for multiple targets. We combine a highest probability data association(HPDA) algorithm for target detection with a particle filter for multiple target tracking. The proposed approach evaluates the probabilities of one-to-one assignments of measurement-to-track and the measurement with the highest probability is selected to be target- originated, and the measurement is used for probabilistic weight update of particle filtering. The performance of the proposed algorithm for target tracking in clutter is compared with the existing clustering algorithm and the sequential monte carlo method for probability hypothesis density(SMC PHD) algorithm for multi-target detection and tracking. Computer simulation studies demonstrate that the HPDA algorithm is robust in performing automatic detection and tracking for multiple targets even though the environment is hostile in terms of high clutter density and low target detection probability.

An Image Processing Algorithm for Detection and Tracking of Aerial Vehicles in Short-Range (무인항공기의 근거리 비행체 탐지 및 추적을 위한 영상처리 알고리듬)

  • Cho, Sung-Wook;Huh, Sung-Sik;Shim, Hyun-Chul;Choi, Hyoung-Sik
    • Journal of the Korean Society for Aeronautical & Space Sciences
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    • v.39 no.12
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    • pp.1115-1123
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    • 2011
  • This paper proposes an image processing algorithms for detection and tracking of aerial vehicles in short-range. Proposed algorithm detects moving objects by using image homography calculated from consecutive video frames and determines whether the detected objects are approaching aerial vehicles by the Probabilistic Multi-Hypothesis Tracking method(PMHT). This algorithm can perform better than simple color-based detection methods since it can detect moving objects under complex background such as the ground seen during low altitude flight and consider the characteristics of vehicle dynamics. Furthermore, it is effective for the flight test due to the reduction of thresholding sensitivity against external factors. The performance of proposed algorithm is verified by applying to the onboard video obtained by flight test.