Abstract
Convolutional neural networks (CNNs) have become the most vigorous technique for a variety of different tasks in computer vision, due to their proficiency in automatically learning high-level visual representations for images. In this paper, we investigate the effect of deep neural networks on the accuracy in combo object detection setting. The insufficiency of labeled data, coupled with the uncertainty of spacial distribution and dynamic changes in luminance, creates situations where combo object detection is far more challenging. Using transfer learning, we present a system for combo object detection based on a deep CNN called ComboNN. The proposed ComboNN is pre-trained on a huge auxiliary dataset ImageNet and fine-tuned on our small dataset. The use of data augmentation and regularization technique significantly reduces overfitting and improves the robustness of the ComboNN. Experimental results demonstrate that our system is capable of making reliable prediction on combo object detection in the real-world images, and achieves much better accuracy than the state-of-the-art CNNs.
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References
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)
Deng, J., Dong, W., Socher, R., Li, L., Li, K., Li, F.: ImageNet: a large-scale hierarchical image database. In: Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)
Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Deng, J., Berg, A., Satheesh, S., Su, H., Khosla, A., Li, F: ImageNet large scale visual recognition competition 2012 (ILSVRC2012). In: net.org/challenges/LSVRC (2012)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: 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. IEEE (2014)
Zhou, B., Lapedriza, A., Xiao, J., Torralba, A., Oliva, A.: Learning deep features for scene recognition using places database. In Advances in Neural Information Processing Systems, pp. 487–495 (2014)
Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815–823 (2015)
Bar, Y., Diamant, I., Wolf, L., Lieberman, S., Konen, E., Greenspan, H.: Chest pathology detection using deep learning with non-medical training. In: 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), pp. 294–297. IEEE (2015)
Ciregan, D., Meier, U., Schmidhuber, J.: Multi-column deep neural networks for image classification. In: Computer Vision and Pattern Recognition (CVPR), pp. 3642–3649 (2012)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Pellegrini, S., Ess, A., Schindler, K., Van, L.: You’ll never walk alone: modeling social behavior for multi-target tracking. In: IEEE International Conference on Computer Vision (ICCV), pp. 261–268 (2009)
Baumgartner, T., Mitzel, D., Leibe, B.: Tracking people and their objects. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3658–3665 (2013)
Yamaguchi, K., Berg, A., Ortiz, L., Berg, T.: Who are you with and where are you going. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1345–1352 (2011)
Pellegrini, S., Ess, A., Van Gool, L.: Improving data association by joint modeling of pedestrian trajectories and groupings. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6311, pp. 452–465. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15549-9_33
Zhao, J., Pang, S., Hartill, B., Sarrafzadeh, A.: A combo object model for maritime boat ramps traffic monitoring. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds.) ICONIP 2016. LNCS, vol. 9947, pp. 623–630. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46687-3_69
Everingham, M., Eslami, A., Van, L., Williams, K., Winn, J., Zisserman, A.: The Pascal visual object classes challenge: a retrospective. Int. J. Comput. Vis. 111(1), 98–136 (2015)
Agrawal, P., Girshick, R., Malik, J.: Analyzing the performance of multilayer neural networks for object recognition. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8695, pp. 329–344. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10584-0_22
Razavian, A., Azizpour, H., Sullivan, J., Carlsson, S.: CNN features on-the-shelf: an astounding baseline for recognition. In: Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 512–519 (2014)
Sermanet, P., Eigen, D., Zhang, X., Mathieu, M.F., LeCun, Y.: OverFeat: integrated recognition, localization and detection using convolutional networks. arXiv:1312.6229 (2013)
Van, G., Setio, A., Jacobs, C., Ciompi, F.: On-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans. In: Biomedical Imaging (ISBI), pp. 286–289 (2015)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 (2014)
Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, pp. 315–323 (2011)
Chollet, F., et al.: Keras. https://keras.io (2015)
Bottou, L.: Large-scale machine learning with stochastic gradient descent. In: Lechevallier, Y., Saporta, G. (eds.) Proceedings of COMPSTAT, pp. 177–186. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-7908-2604-3_16
Sutskever, I., Martens, J., Dahl, G., Hinton, G.: On the importance of initialization and momentum in deep learning. In: International Conference on Machine Learning, pp. 1139–1147 (2013)
Miller, G.: WordNet: a lexical database for english. Commun. ACM 38(11), 39–41 (1995)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Chollet, F.: Xception: deep learning with depthwise separable convolutions. arXiv preprint (2016)
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Zhao, J., Ardekani, I.T., Pang, S. (2019). Deep Learning for Combo Object Detection. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Lecture Notes in Computer Science(), vol 11953. Springer, Cham. https://doi.org/10.1007/978-3-030-36708-4_11
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