四帧间差分与光流法结合的目标检测及追踪
[1] 袁国武. 智能视频监控中的运动目标检测和跟踪算法研究[D]. 昆 明: 云南大学, 2012.
Yuan G W. Research on moving objects detection and tracking methods in intelligent visual surveillance system[D]. Kunming: Yunnan University, 2012.
[2] 唐迅. 基于稀疏编码的群体异常行为检测[D]. 哈尔滨: 哈尔滨工 业大学, 2013.
Tang X. Crowd abnormal behavior detection based on sparse coding[D]. Harbin: Harbin Institute of Technology, 2013.
[3] 白晓方, 杨卫, 陈佩珩. 一种改进的运动目标检测与跟踪方法[J]. 电视技术, 2014, 38(1): 180–182.
Bai X F, Yang W, Chen P H. Improved moving object detection and tracking method[J]. Video Engineering, 2014, 38(1): 180–182.
[4] 许俊波. 目标检测与跟踪方法研究[D]. 武汉: 中国地质大学, 2007.
Xu J B. The research of detection and tracking of moving object[ D]. Wuhan: China University of Geosciences, 2007.
[5] 何黄凯, 唐宁九, 李征, 等. 基于LBP 核密度估计的动态目标分 割模型研究[J]. 计算机应用研究, 2012, 29(7): 2719–2721.
He H K, Tang N J, Li Z, et al. Research of model for dynamic object segmentation based on LBP kernel density estimation[J]. Application Research of Computers, 2012, 29(7): 2719–2721.
[6] 田绪婷, 郭丹. 基于Codebook 的运动补偿内插方法[J]. 计算机 工程, 2016, 42(9): 214–219.
Tian X T, Guo D. Motion-compensated interpolation method based on codebook[J]. Computer Engineering, 2016, 42(9): 214–219.
[7] 张金敏, 王斌. 光照快速变化条件下的运动目标检测[J]. 光电工 程, 2016, 43(2): 14–21.
[8] 袁国武, 陈志强, 龚健, 等. 一种结合光流法与三帧差分法的运 动目标检测算法[J]. 小型微型计算机系统, 2013, 34(3): 668–671.
Yuan G W, Chen Z Q, Gong J, et al. A Moving object detection algorithm based on a combination of optical flow and three-frame difference[J]. Journal of Chinese Computer Systems, 2013, 34(3): 668–671.
[9] 王凯, 吴敏, 姚辉, 等. 多帧背景差与Cauchy 模型融合的目标检 测[J]. 光电工程, 2016, 43(10): 12–17.
Wang K, Wu M, Yao H, et al. Target detection method based on multi-frame background subtractionand cauchy model[J]. Opto- Electronic Engineering, 2016, 43(10): 12–17.
[10] 庾鹏, 王旭, 仝天乐, 等. 基于GrabCut 算法和四帧差分法的目 标提取算法[J]. 微型机与应用, 2016, 35(11): 40–42.
Yu P, Wang X, Tong T L, et al. A target extraction algorithm based on GrabCut segmentation algorithm and four frame differencing[ J]. Microcomputer & its Applications, 2016, 35(11): 40–42.
[11] Enkelmann W. Investigations of multigrid algorithms for the estimation of optical flow fields in image sequences[J]. Computer Vision, Graphics, and Image Processing, 1988, 43(2): 150–177.
[12] 任克强, 余启明, 罗会兰. 一种改进的混合高斯模型运动目标检 测算法[J]. 电视技术, 2012, 36(23): 168–171.
Ren K Q, Yu Q M, Luo H L. Improved algorithm of moving objects detection based on gaussian mixture model[J]. Video Engineering, 2012, 36(23): 168–171.
[13] 袁宝红. 基于视频的运动目标检测与跟踪研究[D]. 合肥: 安徽大 学, 2014.
Yuan B H. Research on video moving object detection and tracking[D]. Hefei: Anhui University, 2014.
[14] 胡社教, 葛西旺, 陈宗海. 基于角点特征的KLT 跟踪全景图像拼 接算法[J]. 系统仿真学报, 2007, 19(8): 1742–1744.
Hu S J, Ge X W, Chen Z H. Based on corner feature KLT track panoramic mosaic algorithm[J]. Journal of System Simulation, 2007, 19(8): 1742–1744.
[15] Schmidt R A, Cathey W T. Optical representations for artificial intelligence problems[C]//O-E/lase'86 Symp, Los Angeles: SPIE, 1986: 226–233.
[16] 吴新根, 罗立民. 一种改进的光流场计算方法[J]. 电子学报, 2000, 28(1): 130–131.
Wu X G, Luo L M. An improved method of optical flow estimation[ J]. Acta Electronica Sinica, 2000, 28(1): 130–131.
[17] 刘丹. 视频运动目标检测与跟踪算法的GPU 并行优化[D]. 长沙: 国防科学技术大学, 2013.
Liu D. parallel optimization for video moving object detection and tracking algorithm based on GPU[D]. Changsha: National University of Defense Technology, 2013.
[18] 蔡瑞初, 谢伟浩, 郝志峰, 等. 基于多尺度时间递归神经网络的 人群异常检测[J]. 软件学报, 2015, 26(11): 2884–2896.
Cai R C, Xie W H, Hao Z F, et al. Abnormal crowd detection based on multi-scale recurrent neural network[J]. Journal of Software, 2015, 26(11): 2884–2896.
刘鑫, 金晅宏. 四帧间差分与光流法结合的目标检测及追踪[J]. 光电工程, 2018, 45(8): 170665. Liu Xin, Jin Xuanhong. Algorithm for object detection and tracking combined on four inter-frame difference and optical flow methods[J]. Opto-Electronic Engineering, 2018, 45(8): 170665.
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