Blind People Assistance for Object Detection Using AI

[1] Girshick, R., Donahue, J., Darrell, T., Malik, J., (2014). Rich feature hierarchies for accurate object detection and semantic
segmentation, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 580–587.
[2] Glorot, X., Bengio, Y., (2010). Understanding the difficulty of training deep feedforward neural networks., in: Aistats, pp. 249–256.
[3] Goodfellow, I.J., (2013). Piecewise linear multilayer perceptrons and dropout. stat 1050, 22.
[5] He, K., Zhang, X., Ren, S., Sun, J., (2016). Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on
Computer Vision and Pattern Recognition, pp. 770–778.
[6] Jung, K., (2012). Object recognition on mobile devices, in: Consumer Electronics-Berlin (ICCE-Berlin), 2012 IEEE International
Conference on, IEEE. pp. 258–262.
[7] Kingma, D., Ba, J., (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 .
[8] Krizhevsky, A., Sutskever, I., Hinton, G.E., (2012). Imagenet classification with deep convolutional neural networks, in: Advances in
neural information processing systems, pp. 1097–1105.
[9] Le, Q.V., Karpenko, A., Ngiam, J., Ng, A.Y., (2011). Ica with reconstruction cost for efficient overcomplete feature learning, in:
Advances in Neural Information Processing Systems, pp. 1017–1025.
[10] LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.D., (1989). Backpropagation applied to
handwritten zip code recognition. Neural computation 1, 541–551.
[11] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., (1998)a. Gradient-based learning applied to document recognition. Proceedings of the
IEEE 86, 2278–2324