• Acta Optica Sinica
  • Vol. 36, Issue 11, 1115001 (2016)
Qin Jian* and Wang Meihua
Author Affiliations
  • [in Chinese]
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    DOI: 10.3788/aos201636.1115001 Cite this Article Set citation alerts
    Qin Jian, Wang Meihua. Fast Pedestrian Proposal Generation Algorithm Using Online Gaussian Model[J]. Acta Optica Sinica, 2016, 36(11): 1115001 Copy Citation Text show less
    References

    [1] Dalal N, Triggs B. Histograms of oriented gradients for human detection[C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005, 1: 886-893.

    [2] Felzenszwalb P, Grishick R B, McAllister D, et al. Object detection with discriminatively trained part-based models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(9): 1627-1645.

    [3] Horn B K P, Schunck B G. Determining optical flow[J]. Artificial Intelligence, 1981, 17(1-3): 185-203.

    [4] Terzopoulos D. Regularization of inverse visual problems involving discontinuities[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986(4): 413-424.

    [5] Lucas B D, Kanade T. An iterative image registration technique with an application to stereo vision[C]. Proceedings of 7th International Joint Conference on Artificial Intelligence, 1981: 674-679.

    [6] Deng Jinhao. Research of pedestrian detection algorithms based on video[D]. Guangzhou: South China University of Technology, 2011.

    [7] Hsman H E. Hardware-based solutions utilizing random forests for object recognition[M]. ∥Kppen M, Kasabov N, Coinill G. Lecture notes in computer science description. Cham: Springer International Publishing, 2009: 760-767.

    [8] Stauoer C, Grimson W E L, Adaptive background mixture models for real-time tracking[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1999, 2: 2246-2252.

    [9] Arseneau S, Cooperstock J R. Real-time image segmentation for action recognition[C]. Proceedings of the IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, 1999: 86-89.

    [10] Collins R T, Lipton A J, Kanade T. Introduction to the special section on video surveillance[C]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8): 745-746.

    [11] Anderson C H, Bert P J, van der Wal G S. Change detection and tracking using pyramids transformation techniques[C]. SPIE, 1985(579): 72-78.

    [12] Ai Haizhou, Lü Fengjun, Liu Wei, et al. Change detection and segmentation for visual surveillance[J]. Computer Engineering and Applications, 2001, 5: 75-77.

    [13] Uijlings J R R, van de Sande K E A, Gevers T, et al. Selective search for object recognition[J]. International Journal of Computer Vision, 2013, 104 (2): 154-171.

    [14] Tang Qing. Research of threshold segmentation algorithms and pedestrian detection on infrared image[D]. Guangzhou: South China University of Technology, 2010.

    [15] Liu Jian, Liu Yanan, Gao Enyang, et al. Human detection method based on foreground segmentation[J]. Journal of Chinese Computer Systems, 2014, 35(3): 654-658.

    [16] Liu Shumin, Huang Yingping, Zhang Renjie. Pedestrian contour extraction and its recognition using stereovision and snake models[J]. Acta Optica Sinica, 2014, 34(5): 0533001.

    [17] Gu Cheng, Qian Weixian, Chen Qian, et al. Rapid head detection method based on binocular stereo vision[J]. Chinese J Lasers, 2014, 41(1): 0108001.

    [18] Gerónimo D, López A M. Vision-based pedestrian protection systems for intelligent vehicles[M]. Cham: Springer International Publishing AG, 2014.

    [19] Cheng M M, Zhang Z M, Lin W L, et al. BING: Binarized normed gradients for objectness estimation at 300 fps[C]. IEEE Conference on Computer Vision and Pattern Recognition, 2014: 3286-3293.

    [20] Hogg R V, Craig A T. Introduction to Mathematical Statistics[M]. 5th edition. Upper Saddle River: Prentice Hall, 2004.

    [21] Chen Yin, Ren Kan, Gu Guohua, et al. Moving object detection based on improved single Gaussian background model[J]. Chinese J Lasers, 2014, 41(11): 1109002.

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    Qin Jian, Wang Meihua. Fast Pedestrian Proposal Generation Algorithm Using Online Gaussian Model[J]. Acta Optica Sinica, 2016, 36(11): 1115001
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