IECE Transactions on Emerging Topics in Artificial Intelligence
ISSN: 3066-1676 (Online) | ISSN: 3066-1668 (Print)
Email: [email protected]
[1] Bay, H., Tuytelaars, T., & Van Gool, L. (2006). Surf: Speeded up robust features. In Computer Vision–ECCV 2006: 9th European Conference on Computer Vision, Graz, Austria, May 7-13, 2006. Proceedings, Part I 9 (pp.404-417). Springer Berlin Heidelberg.
[2] Cai, Z., Saberian, M., & Vasconcelos, N. (2015).Learning complexity-aware cascades for deep pedestrian detection. In Proceedings of the IEEE international conference on computer vision (pp. 3361-3369).
[3] Chen, X., Kundu, K., Zhu, Y., Berneshawi, A. G., Ma, H., Fidler, S., & Urtasun, R. (2015). 3d object proposals for accurate object class detection. Advances in neural information processing systems, 28.
[4] Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., & Wei, Y. (2017). Deformable convolutional networks. In Proceedings of the IEEE international conference on computer vision (pp. 764-773).
[5] Dollár, P., Appel, R., Belongie, S., & Perona, P. (2014). Fast feature pyramids for object detection. IEEE transactions on pattern analysis and machine intelligence,36(8), 1532-1545.
[6] Dollar, P., Wojek, C., Schiele, B., & Perona, P. (2011).Pedestrian detection: An evaluation of the state of the art. IEEE transactions on pattern analysis and machine intelligence, 34(4), 743-761.
[7] Everingham, M., Van Gool, L., Williams, C. K., Winn, J., & Zisserman, A. (2010). The pascal visual object classes (voc) challenge. International journal of computer vision, 88, 303-338.
[8] Geiger, A., Lenz, P., & Urtasun, R. (2012, June). Are we ready for autonomous driving? the kitti vision benchmark suite. In 2012 IEEE conference on computer vision and pattern recognition (pp. 3354-3361). IEEE.
[9] Geiger, A., Wojek, C., & Urtasun, R. (2011). Joint 3d estimation of objects and scene layout. Advances in Neural Information Processing Systems, 24.
[10] Girshick, R. (2015). Fast r-cnn. In Proceedings of the IEEE international conference on computer vision (pp.1440-1448).
[11] 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).
[12] Glorot, X., Bordes, A., & Bengio, Y. (2011, June). Deep sparse rectifier neural networks. In Proceedings of the fourteenth international conference on artificial intelligence and statistics (pp. 315-323). JMLR Workshop and Conference Proceedings.
[13] He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2017). Mask r-cnn. In Proceedings of the IEEE international conference on computer vision (pp. 2961-2969).
[14] Hearst, M. A., Dumais, S. T., Osuna, E., Platt, J., & Scholkopf, B. (1998). Support vector machines. IEEE Intelligent Systems and their applications, 13(4), 18-28.
[15] Hosang, J., Benenson, R., Dollár, P., & Schiele, B.(2015). What makes for effective detection proposals?.IEEE transactions on pattern analysis and machine intelligence, 38(4), 814-830.
[16] Ioffe, S., & Szegedy, C. (2015, June). Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning (pp. 448-456). pmlr.
[17] Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., ... & Darrell, T. (2014, November). Caffe: Convolutional architecture for fast feature embedding. In Proceedings of the 22nd ACM international conference on Multimedia (pp. 675-678).
[18] Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
[19] Li, B., Wu, T., & Zhu, S. C. (2014). Integrating context and occlusion for car detection by hierarchical and-or model. In Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part VI 13 (pp. 652-667). Springer International Publishing.
[20] Lin, T. Y., Dollár, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017). Feature pyramid networks for object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2117-2125).
[21] Lin, T. Y., Goyal, P., Girshick, R., He, K., & Dollár,P. (2017). Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision (pp. 2980-2988).
[22] Cruz-Mota, J., Bogdanova, I., Paquier, B., Bierlaire, M., & Thiran, J. P. (2012). Scale invariant feature transform on the sphere: Theory and applications. International journal of computer vision, 98, 217-241.
[23] Cheng, L., Wang, Y., Liu, Q., Epema, D. H., Liu, C.,Mao, Y., & Murphy, J. (2021). Network-aware locality scheduling for distributed data operators in data centers. IEEE Transactions on Parallel and Distributed Systems, 32(6), 1494-1510.
[24] Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed,S., Fu, C. Y., & Berg, A. C. (2016). Ssd: Single shot multibox detector. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14 (pp. 21-37). Springer International Publishing.
[25] Nezamabadi-pour, H., & Kabir, E. (2004). Image retrieval using histograms of uni-color and bi-color blocks and directional changes in intensity gradient. Pattern Recognition Letters, 25(14), 1547-1557.
[26] Ohn-Bar, E., & Trivedi, M. M. (2015). Learning to detect vehicles by clustering appearance patterns. IEEE Transactions on Intelligent Transportation Systems, 16(5), 2511-2521.
[27] Paisitkriangkrai, S., Shen, C., & van den Hengel, A.(2015). Pedestrian detection with spatially pooled features and structured ensemble learning. IEEE transactions on pattern analysis and machine intelligence, 38(6), 1243-1257.
[28] Pepik, B., Stark, M., Gehler, P., & Schiele, B. (2015).Multi-view and 3d deformable part models. IEEE transactions on pattern analysis and machine intelligence,37(11), 2232-2245.
[29] Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 779-788).
[30] Ren, S., He, K., Girshick, R., & Sun, J. (2015). Fasterr-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28.
[31] Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh,S., Ma, S., ... & Fei-Fei, L. (2015). Imagenet large scale visual recognition challenge. International journal of computer vision, 115, 211-252.
[32] Tan, M., Pang, R., & Le, Q. V. (2020). Efficientdet: Scalable and efficient object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 10781-10790).
[33] Tian, Y., Luo, P., Wang, X., & Tang, X. (2015).Deep learning strong parts for pedestrian detection. In Proceedings of the IEEE international conference on computer vision (pp. 1904-1912).
[34] Wang, X., Yang, M., Zhu, S., & Lin, Y. (2013). Regionlets for generic object detection. In Proceedings of the IEEE international conference on computer vision(pp. 17-24).
[35] Xiang, Y., Choi, W., Lin, Y., & Savarese, S. (2015).Data-driven 3d voxel patterns for object category recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1903-1911).
[36] Yang, F., Choi, W., & Lin, Y. (2016). Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers.In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2129-2137).
[37] Wang, C., Wang, Y., Han, Y., Song, L., Quan, Z.,Li, J., & Li, X. (2017, January). CNN-based object detection solutions for embedded heterogeneous multicore SoCs. In 2017 22nd Asia and South Pacific design automation conference (ASP-DAC) (pp. 105-110).IEEE.
[38] Zhang, S., Benenson, R., & Schiele, B. (2015, June).Filtered channel features for pedestrian detection. In CVPR (Vol. 1, No. 2, p. 4).
[39] Zhu, Y., Urtasun, R., Salakhutdinov, R., & Fidler,S. (2015). segdeepm: Exploiting segmentation and context in deep neural networks for object detection.In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 4703-4711).
IECE Transactions on Emerging Topics in Artificial Intelligence
ISSN: 3066-1676 (Online) | ISSN: 3066-1668 (Print)
Email: [email protected]
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