Fast Feature Pyramids for Object Detection - Estadísticas de Google Académico

Artículos que citan Fast Feature Pyramids for Object Detection

Título / Autor	Año
High-speed tracking with kernelized correlation filters JF Henriques, R Caseiro, P Martins, J Batista IEEE transactions on pattern analysis and machine intelligence 37 (3), 583-596	2014
Feature pyramid networks for object detection TY Lin, P Dollár, R Girshick, K He, B Hariharan, S Belongie Proceedings of the IEEE conference on computer vision and pattern …	2017
Ten years of pedestrian detection, what have we learned? R Benenson, M Omran, J Hosang, B Schiele European Conference on Computer Vision, 613-627	2014
A unified multi-scale deep convolutional neural network for fast object detection Z Cai, Q Fan, RS Feris, N Vasconcelos european conference on computer vision, 354-370	2016
Long-term correlation tracking C Ma, X Yang, C Zhang, MH Yang Proceedings of the IEEE conference on computer vision and pattern …	2015
Reading text in the wild with convolutional neural networks M Jaderberg, K Simonyan, A Vedaldi, A Zisserman International Journal of Computer Vision 116 (1), 1-20	2016
Is faster r-cnn doing well for pedestrian detection? L Zhang, L Lin, X Liang, K He European conference on computer vision, 443-457	2016
Multi-view face detection using deep convolutional neural networks SS Farfade, MJ Saberian, LJ Li Proceedings of the 5th ACM on International Conference on Multimedia …	2015
Synthetic data for text localisation in natural images A Gupta, A Vedaldi, A Zisserman Proceedings of the IEEE Conference on Computer Vision and Pattern …	2016
Person re-identification: Past, present and future L Zheng, Y Yang, AG Hauptmann arXiv preprint arXiv:1610.02984	2016
Hypernet: Towards accurate region proposal generation and joint object detection T Kong, A Yao, Y Chen, F Sun Proceedings of the IEEE conference on computer vision and pattern …	2016
Motchallenge 2015: Towards a benchmark for multi-target tracking L Leal-Taixé, A Milan, I Reid, S Roth, K Schindler arXiv preprint arXiv:1504.01942	2015
3d object proposals for accurate object class detection X Chen, K Kundu, Y Zhu, AG Berneshawi, H Ma, S Fidler, R Urtasun Advances in Neural Information Processing Systems, 424-432	2015
MOT16: A benchmark for multi-object tracking A Milan, L Leal-Taixé, I Reid, S Roth, K Schindler arXiv preprint arXiv:1603.00831	2016
Learning to track: Online multi-object tracking by decision making Y Xiang, A Alahi, S Savarese Proceedings of the IEEE international conference on computer vision, 4705-4713	2015
Filtered channel features for pedestrian detection. S Zhang, R Benenson, B Schiele CVPR 1 (2), 4	2015
Local decorrelation for improved pedestrian detection W Nam, P Dollár, JH Han Advances in Neural Information Processing Systems, 424-432	2014
Taking a deeper look at pedestrians J Hosang, M Omran, R Benenson, B Schiele Proceedings of the IEEE Conference on Computer Vision and Pattern …	2015
Pedestrian detection aided by deep learning semantic tasks Y Tian, P Luo, X Wang, X Tang Proceedings of the IEEE Conference on Computer Vision and Pattern …	2015
How far are we from solving pedestrian detection? S Zhang, R Benenson, M Omran, J Hosang, B Schiele Proceedings of the iEEE conference on computer vision and pattern …	2016

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