基于超像素分割和混合权值AdaBoost运动检测算法
[1] SHI X B, WANG M, ZHANG D Y, et al. An approach for moving object detection using continuing tracking optical flow[J].Journal of Chinese Computer Systems, 2014, 35(3): 642-647.
[2] LI T C, VILLARRUBIA G, SUN S D, et al. Resampling methods for particle filtering: identical distribution, a new method, and comparable study[J]. Frontiers of Information Technology and Electronic Engineering, 2015, 16(11): 969-984.
[3] AVIDAN S. Ensemble tracking [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(2): 261-27.
[4] LIU B Y, HUANG J Z, YANG L, et al. Robust tracking using local sparse appearance model and k-selection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (S0162-8828), 2013, 35(12): 2968-2981.
[5] YANG F, LU H, YANG M H. Robust superpixel tracking[C]//IEEE Transactions on Image Processing, 2014: 1057-7149.
[6] 莫邵文, 邓新蒲, 王帅, 等. 基于改进视觉背景提取的运动目标检测算法[J]. 光学学报, 2016, 36(6): 204-213.
[7] WANG S, LU H C, YANG F, et al. Superpixel tracking[C]//International Conference on Computer Vision: ICCV, 2011, 24(4): 1323-1330.
[8] ZHOU X, LI X, HU W M. Learning a superpixel-driven speed function for level set tracking[C]//IEEE Transactions on Cybernetics: TCYB, 2015, 46(7): 2168-2267.
[9] LIU J J, CHEN Y, ZHA C, et al. Tracking using superpixel features[C]//Eighth International Conference on Measuring Technology and Mechatronics Automation: ICMTMA, 2016, 211: 878-881.
[10] HUANG G H, PUN C M, LIN C. Video object tracking using interactive segmentation and superpixel based Gaussian kernel [C]//The 19th International Conference on Information Visualization: iV, doi: 10. 1109/iv. 2015. 81.
[11] GAO J, LING H B, HU W M, et al. Transfer learning based visual tracking with Gaussian processes regression[C]//Proceedings of ECCV, Zürich, Switzerland, 2014: 188-203.
[12] LI X, HAN Z, WANG L, et al. Visual tracking via random walks on graph model[J]. IEEE Transactions on Cybernet, 2016, 46(9): 2144-2155.
[13] NAM H, HAN B. Learning multi-domain convolutional neural networks for visual tracking[C]//Proceedings of CVPR, Las Vegas, NV, USA, 2016: 4293-4302.
[14] 刘龙, 樊波阳, 刘金星, 等. 面向运动目标检测的粒子滤波视觉注意力模型[J]. 电子学报, 2016, 44(9): 2235-2241.
李忠海, 杨超, 梁书浩. 基于超像素分割和混合权值AdaBoost运动检测算法[J]. 电光与控制, 2018, 25(2): 33. LI Zhonghai, YANG Chao, LIANG Shujie. AdaBoost Moving-target Detection Algorithm Based on Superpixel Segmentation and Mixed Weight[J]. Electronics Optics & Control, 2018, 25(2): 33.