[1] Chen X. Three-dimensional plane target based on neural network recognition[D]. Changchun: Changchun University of Science and Technology, 7-9(2011).
[3] Dalal N, Triggs B. Histograms of oriented gradients for human detection. [C]∥2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), June 20-25, 2005, San Diego, CA, USA. New York: IEEE, 886-893(2005).
[4] Schuldt C, Laptev I, Caputo B. Recognizing human actions: a local SVM approach. [C]∥Proceedings of the 17th International Conference on Pattern Recognition, August 26, 2004, Cambridge, UK. New York: IEEE, 3, 32-36(2004).
[8] Socher R, Huval B, Bath B et al. Convolutional-recursive deep learning for 3D object classification. [C]∥Proceedings of the 25th International Conference on Neural Information Processing Systems, December 3-6, 2012, Lake Tahoe, Nevada. USA: Curran Associated Inc., 1, 656-664(2012).
[9] Eitel A, Springenberg J T, Spinello L et al. Multimodal deep learning for robust RGB-D object recognition. [C]∥2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), September 28-October 2, 2015, Hamburg, Germany. New York: IEEE, 681-687(2015).
[10] Li W. Research on RGB-D object recognition via feature learning[D]. Wuhan: Huazhong University of Science and Technology, 24-28(2016).
[11] Xu X Y, Li Y C, Wu G S et al. Multi-modal deep feature learning for RGB-D object detection[J]. Pattern Recognition, 72, 300-313(2017).
[14] Redmon J, Farhadi A. YOLO9000: better, faster, stronger. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI. New York: IEEE, 6517-6525(2017).
[16] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. [C]∥Proceedings of the 25th International Conference on Neural Information Processing Systems, December 3-6, 2012, Lake Tahoe, Nevada. New York: ACM, 1, 1097-1105(2012).
[18] Liu W, Anguelov D, Erhan D et al. SSD: single shot MultiBox detector[M]. ∥Leibe B, Matas J, Sebe N,
[19] Silberman N, Hoiem D, Kohli P et al. Indoor segmentation and support inference from RGBD images[M]. ∥Fitzgibbon A, Lazebnik S, Perona P Berlin,