參考文獻 |
[1] S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.39, is.6, pp.1137-1149, 2016.
[2] Z. Zhang, “Microsoft kinect sensor and its effect,” IEEE MultiMedia, vol.19, no.2, pp.4-10, Feb. 2012.
[3] M. Sotelo, J. Barriga, D. Fernandez, I. Parra, J. Naranjo, M. Marron, S. Alvarez, and M. Gavilan, "Vision-based blind spot detection using optical flow," Lecture Notes in Computer Science, vol.4739, pp.1113-1118, 2007.
[4] C. Braillon, C. Pradalier, J. Crowley, C. Laugier, L. Gravir, and I. Rhone-alpes, “Real-time moving obstacle detection using optical flow models,” in Proc. Intelligent Vehicles Symp., Tokyo, Japan, Jun.13-15, 2006, pp.466-471.
[5] K. Yamaguchi, “Vehicle ego-motion estimation and moving object detection using a monocular camera,” in Proc. 18th Int. Conf. on Pattern Recognition, Hong Kong, China, Aug.22-24, 2006, pp.610-613.
[6] D. Hoiem, A. Efros, and M. Hebert, "Putting objects in perspective," Int. Journal of Computer Vision, vol.80, no.1, pp.3-15, 2008.
[7] A. Saxena, S. Chung, and A. Ng, "3-D depth reconstruction from a single still image," Int. Journal of Computer Vision, vol.76, no.1, pp.53-69, 2008.
[8] M. Collins, R. Schapire, and Y. Singer, “Logistic regression, adaboost and bregman distances,” in Proc. the 13th Annual Conf. on Computational Learning Theory, San Francisico, CA, Jun.27-Jul.1, 2000, pp.1-26.
[9] S. Zhang, C. Wang, S. Chan, X. Wei, and C. Ho, “New object detection, tracking, and recognition approaches for video surveillance over camera network,” IEEE Sensors Journal, vol.15, no.5, pp.2679-2691, 2015.
[10] D. Comaniciu, P. Meer, and S. Member, “Mean shift?: a robust approach toward feature space analysis,” IEEE Trans. on Pattern Anal. and Mach. Intell., vol.24, no.5, pp.603-619, 2002.
[11] Z. Zivkovic and F. DerHeijden, “Efficient adaptive density estimation per image pixel for the task of background subtraction,” Pattern Recognition Letters, vol.27, no.7, pp.773-780, 2006.
[12] S. Chan, B. Liao, K. Tsui, P. Road, and H. Kong, “Bayesian Kalman filtering, regularization and compressed sampling,” in Proc. IEEE Conf. on Circuits and Systems (MWSCAS), Seoul, South Korea, Aug.7-10, 2011, pp.1-4.
[13] H.-S. Sandhu, K.-J. Singh, and D.-S. Kapoor, “Automatic edge detection algorithm and area calculation for flame and fire images,” in Proc. IEEE Conf. on Cloud System and Big Data Engineering, Noida, India, Jan.14-15, 2016, pp.403-407.
[14] A. Krizhevsky, I. Sutskever, and G. Hinton, “ImageNet classification with deep convolutional neural networks,” in Proc. of Neural Information Processing Systems 2012 (NIPS 2012), Lake Tahoe, Nevada, Dec.3-8, 2012, pp.1-9.
[15] D. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. Journal of Computer Vision (IJCV), vol.60, is.2, pp.91-110, 2004.
[16] H. Bay, A. Ess, T. Tuytelaars, L. Gool, "SURF: Speeded up robust features", Computer Vision and Image Understanding (CVIU), vol.110, No.3, pp.346–359, 2008.
[17] C. Harris and M. Stephens, “A combined corner and edge detector,” in Proc. 4th Alvey Vision Conf., Manchester, UK, Aug.30-Sep.2, 1988, pp.147-152.
[18] J. Shi and C. Tomasi, “Good features to track” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, Jun.21-23, 1994, pp.593-600.
[19] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proc. of IEEE Int. Conf. on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, Jun.27-30, 2016, pp.779-788.
[20] W. Liu, D.Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, ”SSD: Single shot multibox detector,” in European Conf. on Computer Vision (ECCV), Amsterdam, Holland, Oct.8-16, 2016, pp.21-37.
[21] R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, Jun.23-28, 2014, pp.580-587.
[22] R. Girshick, “Fast R-CNN,” in Proc. of IEEE Int. Conf. on Computer Vision (ICCV), Santiago, Chile, Dec.11-18, 2015, pp.1440-1448.
[23] J. Uijlings, K. Sande, T. Gevers, and A. Smeulders, “Selective search for object recognition,” Int. Journal of Computer Vision (IJCV), vol.104, is.2, pp.154-171, 2013.
[24] J. Aceituno, R. Arnay, J. Toledo, and Leopoldo Acosta, “Using kinect on an autonomous vehicle for outdoors obstacle detection,” IEEE Sensor Journal, vol.16, no.10, May 15, 2016.
[25] J. Choi, D. Kim, H. Yoo, and K. Sohn, “Rear obstacle detection system based on depth from Kinect,” in Proc. 15th Int. IEEE Conf. Intelligent Transportation Systems (ITSC), Sep.16-19, 2012, pp. 98-101.
[26] N. Silberman, D. Hoiem, P. Kohli, and R. Fergus, "Indoor segmentation and support inference from RGBD images," in Proc. European Conf. on Computer Vision (ECCV), Florence, Italy, Oct.7-13, 2012, pp.746-760.
[27] S. Gupta, R. Girshick, P. Arbelaez, and J. Malik, "Learning rich features from RGB-D images for object detection and segmentation," in Proc. European Conf. on Computer Vision (ECCV), Zurich, Switzerland, Sep.6-12, 2014, pp.345-360.
[28] A. Eitel, J. Springenberg, L. Spinello, M. Riedmiller, W. Burgard, “Multimodal deep learning for robust RGB-D object recognition,” in Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), Hamburg, Sep.28-Oct.2, 2015, pp.681-687.
[29] Z. Deng and L. Latecki, "Amodal detection of 3D objects: inferring 3D bounding boxes from 2D ones in RGB-depth images," in Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, 2017, pp.398-406.
[30] X. Xu, Y. Li, G. Wu, and J. Luo, "Multi-modal deep feature learning for RGB-D object detection," Pattern Recognition, vol.72, pp.300-313, 2017.
[31] J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Boston, MA, Jun.8-10, 2015, pp.3431-3440.
[32] K. He, X. Zhang, S. Ren, and J. Sun, “Spatial pyramid pooling in deep convolutional networks for visual recognition,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol.37, Is.9, pp.1904-1916, 2015.
[33] M. Zeiler and R.Fergus, “Visualizing and understanding convolutional networks,” in Proc. European Conf. on Computer Vision (ECCV), Zurich, Switzerland, Sep.6-12, 2014, pp.818-833.
[34] A. Krizhevsky, I. Sutskever and G. Hinton, “ImageNet classification with deep convolutional neural networks,” in NIPS Proc. Int.l Conf. on Neural Information Processing Systems (NIPS), Lake Tahoe, Nevada, Dec.03-06, 2012, pp.1097-1105.
[35] Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, “Caffe: Convolutional architecture for fast feature embedding,” in Proc. of the 22nd ACM Int. Conf. on Multimedia, Orlando, FL, 2014, pp.675-678. |