Wu Y, Lim J, and Yang M H. Online object tracking: a benchmark[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Portland, USA, 2013: 2411-2418.
|
Ross D A, Lim J, Lin R S, et al.. Incremental learning for robust visual tracking[J]. International Journal of Computer Vision, 2008, 77(3): 125-141.
|
Zhang K, Zhang L, and Yang M H. Fast compressive tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(10): 2002-2015.
|
Zhong W, Lu H C, and Yang M H. Robust object tracking via sparse collaborative appearance model[J]. IEEE Transactions on Image Processing, 2014, 23(5): 2356-2368.
|
陈思, 苏松志, 李绍滋, 等. 基于在线半监督boosting的协同训练目标跟踪算法[J]. 电子与信息学报, 2014, 36(4): 888-895.
|
Chen S, Su S Z, Li S Z, et al.. A novel co-training object tracking algorithm based on online semi-supervised boosting[J]. Journal of Electronics Information Technology, 2014, 36(4): 888-895.
|
Zhang K, Zhang L, Liu Q, et al.. Fast tracking via dense spatio-temporal context learning[C]. Proceedings of European Conference on Computer Vision, Zurich, Switzerland, 2014: 127-141.
|
Kalal Z, Mikolajczyk K, and Matas J. Tracking- learning-detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(7): 1409-1422.
|
Babenko B, Yang M H, and Belongie S. Robust object tracking with online multiple instance learning[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(8): 1619-1632.
|
Zhang K H and Song H H. Real-time visual tracking via online weighted multiple instance learning[J]. Pattern Recognition, 2013, 46(1): 397-411.
|
陈东成, 朱明, 高文, 等. 在线加权多示例学习实时目标跟踪[J]. 光学 精密工程, 2014, 22(6): 1661-1667.
|
Chen D C, Zhu M, Gao W, et al.. Real-time object tracking via online weighted multiple instance learning[J]. Optics and Precision Engineer, 2014, 22(6): 1661-1667.
|
宁纪锋, 赵耀博, 石武祯. 多通道Haar-like特征多示例学习目标跟踪[J]. 中国图象图形学报, 2014, 19(7): 1038-1045.
|
Ning J F, Zhao Y B, and Shi W Z. Multiple instance learning based object tracking with multi-channel haar-like feature[J]. Journal of Image and Graphics, 2014, 19(7): 1038-1045.
|
郑胤, 陈权崎, 章毓晋. 深度学习及其在目标和行为识别中的新进展[J]. 中国图像图形学报, 2014, 19(2): 175-184.
|
Zheng Y, Chen Q, and Zhang Y. Deep learning and its new progress in object and behavior recognition[J]. Journal of Image and Graphics, 2014, 19(2): 175-184.
|
Vincent P, Larochellel H, Lajoie I, et al.. Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion[J]. Journal of Machine Learning Research, 2010, 11: 3371-3408.
|
程帅, 曹永刚, 孙俊喜, 等. 基于增强群跟踪器和深度学习的目标跟踪[J]. 电子与信息学报, 2015, 37(7): 1646-1653.
|
Cheng S, Cao Y G, Sun J X, et al.. Target tracking based on enhanced flock of tracker and deep learning[J]. Journal of Electronics Information Technology, 2015, 37(7): 1646-1653.
|
Hinton G E and Salakhutdinov R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504-507.
|
Rob H and Alan F. Discriminatively trained particle filters for complex multi-object tracking[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Miami, USA, 2009: 240-247.
|
李天成, 孙树栋. 采用双重采样的移动机器人Monte Carlo定位方法[J]. 自动化学报, 2010, 36(9): 1279-1286.
|
Li T C and Sun S D. Double-resampling based monte carlo localization for mobile robot[J]. Acta Automatica Sinica, 2010, 36(9): 1279-1286.
|
Torralba A, Fergus R, and Freeman W T. 80 million tiny images: a large data set for nonparametric object and scene recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(11): 1958-1970.
|
Olshausen B and Field D. Sparse coding with an overcomplete basis set: a strategy employed by V1[J]. Vision Research, 1997, 37(23): 3311-3326.
|
Dinh T B, Vo N, and Medion G. Context tracker: exploring supporters and distracters in unconstrained environments[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Providence, USA, 2011: 1177-1184.
|
Zhang T, Ghanem B, Liu S, et al.. Robust visual tracking via multi-task sparse learning[C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Providence, USA, 2012: 2042-2049.
|