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背景帧间差分法的移动目标跟踪研究 被引量:3

Research on Moving Target Tracking Based on Background Interframe Difference Method
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摘要 根据视觉领域中对运动目标跟踪分析的热点算法,提出背景帧间差分算法对视频中移动的行人进行目标跟踪。该算法通过计算连续三帧视频图像间各相应像素点的差分,求出背景点和目标点,再经过形态学运算即可描绘出移动的目标。实验证明,该算法简单、有效,且不易受光照影响;能满足大部分视频中对移动目标的跟踪。 According to the hotspot algorithm for moving pedestrian tracking and analyzing in the visual field,a background interframe difference algorithm is proposed.The algorithm calculates the difference between the corresponding pixel points of three consecutive frames of video images,finds the background point and the target point,and then depict moving pedestrian targets by morphological operations.Experiments show that the algorithm is simple,effective,and not easily affected by illumination,and can satisfy most tracking to the moving target in video.
作者 黄金海
出处 《中国仪器仪表》 2019年第1期62-65,共4页 China Instrumentation
关键词 背景帧间差分 目标跟踪 算法 Background Interframe Difference Object tracking Algorithm
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  • 1Text Retrieval Conference (TREC) [Online], available: http://trec.nist.gov/, April 5, 2016.
  • 2National Institute of Standards and Technology (NIST) [Online], available: http://www.nist.gov/index.html, April 5, 2016.
  • 3TREC Video Retrieval Evaluation (TRECVID) [Online], available: http://www-nlpir.nist.gov/projects/trecvid/, Ap- ril 5, 2016.
  • 4Dollar P, Wojek C, Schiele B, Perona P. Pedestrian detec- tion: an evaluation of the state of the art. IEEE Transac- tions on Pattern Analysis and Machine Intelligence, 2012, 34(4): 743--761.
  • 5Benenson R, Omran M, Hosang J, Schiele B. Ten years of pedestrian detection, what have we learned? In: Proceed- ings of the 12th European Conference on Computer Vision. Zurich, Switzerland: Springer, 2014. 613-627.
  • 6Dalal N, Triggs B. Histograms of oriented gradients for hu- man detection. In: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recog- nition. San Diego, USA: IEEE, 2005. 886-893.
  • 7Felzenszwalb P, McAllester D, Ramanan D. A discrimina- tively trained, multiscale, deformable part model. In: Pro- ceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition. Anchorage, Alaska, USA: IEEE, 2008. 1-8.
  • 8Ouyang W, Wang X. Joint deep learning for pedestrian de- tection. In: Proceedings of the 2013 IEEE International Conference on Computer Vision. Sydney, Australia: IEEE, 2013. 2056-2063.
  • 9Luo P, Tian Y, Wang X, Tang X. Switchable deep network for pedestrian detection. In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, Ohio, USA: IEEE, 2014. 899-906.
  • 10Hosang J, Omran M, Benenson R, Schiele B. Taking a deeper look at pedestrians. In: Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recog- nition. Boston, USA: IEEE, 2015. 4073-4082.

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