Leveraging RGB-D Data: Adaptive fusion and domain adaptation for object detection
暂无分享,去创建一个
[1] Hans-Hellmut Nagel,et al. Model-Based Object Tracking in Traffic Scenes , 1992, ECCV.
[2] Dariu Gavrila,et al. A Multilevel Mixture-of-Experts Framework for Pedestrian Classification , 2011, IEEE Transactions on Image Processing.
[3] David A. McAllester,et al. A discriminatively trained, multiscale, deformable part model , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[4] Bernt Schiele,et al. Multi-cue onboard pedestrian detection , 2009, CVPR.
[5] Luc Van Gool,et al. The 2005 PASCAL Visual Object Classes Challenge , 2005, MLCW.
[6] Andrew W. Fitzgibbon,et al. Real-time human pose recognition in parts from single depth images , 2011, CVPR 2011.
[7] Christopher K. I. Williams,et al. Pascal Visual Object Classes Challenge Results , 2005 .
[8] Luc Van Gool,et al. SURF: Speeded Up Robust Features , 2006, ECCV.
[9] Ashutosh Saxena,et al. A Fast Data Collection and Augmentation Procedure for Object Recognition , 2008, AAAI.
[10] Matthijs C. Dorst. Distinctive Image Features from Scale-Invariant Keypoints , 2011 .
[11] Roland Siegwart,et al. Multiclass Multimodal Detection and Tracking in Urban Environments * , 2009, FSR.
[12] Dariu Gavrila,et al. Monocular Pedestrian Detection: Survey and Experiments , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[13] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[14] James M. Rehg,et al. Real-time human detection using contour cues , 2011, 2011 IEEE International Conference on Robotics and Automation.
[15] Roland Siegwart,et al. Segmentation and Unsupervised Part-based Discovery of Repetitive Objects , 2010, Robotics: Science and Systems.
[16] Lorenzo Torresani,et al. Exploiting weakly-labeled Web images to improve object classification: a domain adaptation approach , 2010, NIPS.
[17] Bill Triggs,et al. Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[18] Sebastian Thrun,et al. Towards 3D object recognition via classification of arbitrary object tracks , 2011, 2011 IEEE International Conference on Robotics and Automation.
[19] Gunnar Rätsch,et al. An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis , 2008, NIPS.
[20] P. Bartlett,et al. Probabilities for SV Machines , 2000 .
[21] Dieter Fox,et al. Laser and Vision Based Outdoor Object Mapping , 2008, Robotics: Science and Systems.
[22] Roland Siegwart,et al. A Layered Approach to People Detection in 3D Range Data , 2010, AAAI.
[23] Kai Oliver Arras,et al. People detection in RGB-D data , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.
[24] Wolfram Burgard,et al. Classifying dynamic objects , 2009, Auton. Robots.
[25] Bernt Schiele,et al. Pedestrian detection in crowded scenes , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[26] Robert A. Jacobs,et al. Hierarchical Mixtures of Experts and the EM Algorithm , 1993, Neural Computation.
[27] Wolfram Burgard,et al. Classifying Dynamic Objects: An Unsupervised Learning Approach , 2008, Robotics: Science and Systems.
[28] Pietro Perona,et al. Pedestrian detection: A benchmark , 2009, CVPR.
[29] Dariu Gavrila,et al. Multi-cue pedestrian classification with partial occlusion handling , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[30] Geoffrey E. Hinton,et al. Adaptive Mixtures of Local Experts , 1991, Neural Computation.
[31] Peter E. Hart,et al. Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.
[32] Dieter Fox,et al. Object Recognition in 3D Point Clouds Using Web Data and Domain Adaptation , 2010, Int. J. Robotics Res..