1 |
K. Grauman and T. Darrell, "The pyramid match kernel Discriminative classification with sets of image features," Proceedings of IEEE International Conference on Computer Vision, 2005.
|
2 |
C. Cortes and V. Vapnik, "Support-Vector Network," Machine Learning, Vol. 20, No. 3 273-297, 1995.
DOI
|
3 |
Quinlan, J. Ross, C4. 5: programs for machine learning, Elsevier, 2014.
|
4 |
Viola, Paul, and Michael J. Jones. "Robust real-time face detection," International journal of computer vision 57.2 pp. 137-154, 2004.
DOI
|
5 |
Schmidhuber, Jurgen. "Deep learning in neural networks: An overview," Neural Networks 61 pp. 85-117, 2015.
DOI
|
6 |
Dollar, Piotr, et al. "Pedestrian detection: An evaluation of the state of the art," IEEE transactions on pattern analysis and machine intelligence Vol. 34 No. 4 pp. 743-761, 2012.
DOI
|
7 |
Y. B. Jo, et al. "Traffic Sign Recognition using SVM and Decision Tree for Poor Driving Environment," j.inst.Korean.electr.electron.eng Vol. 18 No.4 pp. 485-494, 2014.
|
8 |
Salari, E., and D. Ouyang. "Camera-based Forward Collision and lane departure warning systems using SVM," 2013 IEEE 56th International Midwest Symposium on Circuits and Systems (MWSCAS), IEEE, 2013.
|
9 |
J. Wu and X. Zhang, "A PCA classifier and its application in vehicle detection," in Proc. Int. Joint Conf. Neural Netw., vol. 1, pp. 600-604, 2001.
|
10 |
Z. Sun, G. Bebis, and R. Miller, "On-road vehicle detection using Gabor filters and support vector machines," in Proc. 14th Int. Conf. Digit. Signal Process, pp. 1019-1022, 2002.
|
11 |
Z. Sun, G. Bebis, and R. Miller, "Quantized wavelet features and support vector machines for on-road vehicle detection," in Proc. 7th Int. Conf. Control, Autom., Robot. Vis, pp. 1641-1646, 2002.
|
12 |
Z. Sun, G. Bebis, and R. Miller, "Improving the performance of on-road vehicle detection by combining Gabor and wavelet features," in Proc. IEEE 5th Int. Conf. Intell. Transp. Syst, pp. 130-135, 2002.
|
13 |
R. Lienhart and J. Maydt, "An extended set of Haar-like features for rapid object detection," in Proc. Int. Conf. Image Process, pp. 900-903, Jan. 2002.
|
14 |
R. Lienhart, A. Kuranov, and V. Pisarevsky, "Empirical analysis of detection cascades of boosted classifiers for rapid object detection," in Proc. 25th German Pattern Recognit. Symp. pp. 297-304, 2003.
|
15 |
LeCun, Yann, et al. "Backpropagation applied to handwritten zip code recognition," Neural computation, Vol 1. No. 4: pp. 541-551, 1989.
DOI
|
16 |
Wen, Xuezhi, and Yuhui Zheng. "An improved algorithm based on AdaBoost for vehicle recognition," The 2nd International Conference on Information Science and Engine ering. IEEE, 2010.
|
17 |
X. Wen et. al, "Efficient Feature Selection and Classification for Vehicle Detection," IEEE Trans. On Circuits and Systems for Video Technology, Vol.25, No.3, pp.508-517, Mar. 2015.
DOI
|
18 |
O. Russakovsky et. al, "ImageNet Large Sclae Visual Recognition Challenge," International Journal of Computer Vision, Vol.115, No.3, pp.211-252, Dec.2015.
DOI
|
19 |
LeCun, Yann, et al. "Gradient-based learning applied to document recognition," Proceedings of the IEEE 86.11 pp. 2278-2324, 1998.
DOI
|
20 |
A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," in Advances in Neural Information Processing Systems (NIPS), 2012.
|
21 |
Gao, Yongbin, and Hyo Jong Lee. "Vehicle Make Recognition Based on Convolutional Neural Network," Information Science and Security (ICISS), 2015 2nd International Conference on. IEEE, 2015.
|
22 |
LANGE, Stefan; ULBRICH, Fritz; GOEHRING, Daniel, "Online vehicle detection using deep neural networks and lidar based preselected image patches," Intelligent Vehicles Symposium (IV), pp. 954-959, 2016 IEEE.
|
23 |
Bautista, Carlo Migel, et al. "Convolutional neural network for vehicle detection in low resolution traffic videos," Region 10 Symposium (TENSYMP), 2016 IEEE. IEEE, 2016.
|
24 |
Li, Huimin, et al. "A high performance FPGA-based accelerator for large-scale convolutional neural networks," Field Programmable Logic and Applications (FPL), 2016 26th International Conference on. EPFL, 2016.
|
25 |
Huval, B., Wang, T., Tandon, S., Kiske, J., Song, W.,Pazhayampallil, J., Andriluka, M., Rajpurkar, P., Migimatsu,T., Cheng-Yue, R., Mujica, F., Coates, A., and Ng, A. Y. An "Empirical Evaluation of Deep Learning on Highway Driving," ArXiv e-prints, April 2015.
|
26 |
Jung, Seokwoo, et al. "Real-time Traffic Sign Recognition system with deep convolutional neural network," Ubiquitous Robots and Ambient Intelligence (URAI), 2016 13th International Conference on. IEEE, 2016.
|
27 |
Fukui, Hiroshi, et al. "Pedestrian detection based on deep convolutional neural network with ensemble inference network," 2015 IEEE Intelligent Vehicles Symposium (IV), IEEE, 2015.
|
28 |
Li, Ning, et al, "A multistage dataflow implementation of a Deep Convolutional Neural Network based on FPGA for high-speed object recognition," 2016 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI), IEEE, 2016.
|
29 |
Linjie Yang, Ping Luo, Chen Change Loy, Xiaoou Tang. "A Large-Scale Car Dataset for Fine-Grained Categorization and Verification," Computer Vision and Pattern Recognition (CVPR), 2015.
|
30 |
J. Arrospide, L. Salgado, M. Nieto, "Video analysis based vehicle detection and tracking using an MCMC sampling framework," EURASIP Journal on Advances in Signal Processing, vol. 2012, Article ID 2012:2, Jan. 2012.
|