光学学报, 2016, 36 (11): 1115001, 网络出版: 2016-11-08   

采用在线高斯模型的行人检测候选框快速生成方法

Fast Pedestrian Proposal Generation Algorithm Using Online Gaussian Model
作者单位
重庆大学通信工程学院, 重庆 400030
摘要
行人检测是模式识别及机器学习领域的研究热点之一,广泛应用于智能监控、辅助驾驶等领域,而行人候选框的生成是识别及跟踪行人目标的一项重要的前期工作。针对静态监控场景以及特定情况下的车载监控场景,提出了一种基于在线高斯模型的行人检测候选框的快速生成方法(OL_GMPG)。该方法采用高斯模型拟合行人尺寸分布,可以通过生成较少数目的行人候选框达到较高的检测率;并可通过高斯模型的学习与更新过程,获取场景中行人频繁出现的位置以及对应的目标尺度信息,为后续的行人识别及跟踪过程提供辅助。
Abstract
Pedestrian detection is one of the most active research topics in the fields of pattern recognition and machine learning. It has been widely used in intelligent monitoring, auxiliary driving and so on. Generating pedestrian detection proposals is an important work in the early period of pedestrian recognition and pedestrian tracking. Based on the static monitoring scene as well as the on-board monitoring scene under specific circumstances, a novel method to generate pedestrian detection proposals quickly (OL_GMPG) is proposed by using online Gaussian model. High detection rate can be achieved by generating fewer pedestrian detection proposals through the Gaussian model fitting. Both the positions where people appear most frequently and the scale information of corresponding targets can be obtained through the learning and updating processes of the Gaussian model. The information is beneficial to subsequent pedestrian recognition or pedestrian tracking.
参考文献

[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] 邓锦豪. 基于视频图像的行人检测算法研究[D]. 广州: 华南理工大学, 2011.

    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] 艾海舟, 吕风军, 刘伟, 等. 面向视觉监视的变化检测与分割[J]. 计算机工程与应用, 2001(5): 75-77.

    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] 唐清. 阈值分割及红外图像行人检测研究[D]. 广州: 华南理工大学, 2010.

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

[15] 刘剑, 刘亚楠, 高恩阳, 等. 基于前景分割的行人检测方法[J]. 小型微型计算机系统, 2014, 35(3): 654-658.

    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] 刘述民, 黄影平, 张仁杰. 基于立体视觉及蛇模型的行人轮廓提取及其识别[J]. 光学学报, 2014, 34(5): 0533001.

    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] 顾骋, 钱惟贤, 陈钱, 等. 基于双目立体视觉的快速人头检测方法[J]. 中国激光, 2014, 41(1): 0108001.

    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] 陈银, 任侃, 顾国华, 等. 基于改进的单高斯背景模型运动目标检测算法[J]. 中国激光, 2014, 41(11): 1109002.

    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.

覃剑, 王美华. 采用在线高斯模型的行人检测候选框快速生成方法[J]. 光学学报, 2016, 36(11): 1115001. 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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