Object detection for small pixel in urban roads videos

[1] [1] Lowe D G. Object recognition from local scale-invariant features[C]//The Proceedings of the 7th IEEE International Conference on Computer Vision, 1999, 2: 1150–1157.

[2] [2] Lowe D G. Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision, 2004, 60(2): 91–110.

[3] [3] Dalal N, Triggs B. Histograms of oriented gradients for human detection[C]//Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005, 1: 886–893.

[4] [4] Ojala T, Pietikainen M, Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(7): 971–987.

[5] [5] Cortes C, Vapnik V. Support-vector networks[J]. Machine Learning, 1995, 20(3): 273–297.

[6] [6] Ho T K. Random decision forests[C]//Proceedings of the 3rd International Conference on Document Analysis and Recognition, 1995, 1: 278–282.

[7] [7] Luo Z J, Zeng G Q. Space objects detection in video satellite images using improved MTI algorithm[J]. Opto-Electronic Engineering, 2018, 45(8): 180048.

[8] [8] Fan X S, Xu Z Y, Zhang J L. Dim small target tracking based on improved particle filter[J]. Opto-Electronic Engineering, 2018, 45(8): 170569.

[9] [9] Schroff F, Kalenichenko D, Philbin J. FaceNet: a unified embedding for face recognition and clustering[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition, 2015: 815–823.

[10] [10] Wang X H, Gao L L, Wang P, et al. Two-stream 3-D convNet fusion for action recognition in videos with arbitrary size and length[J]. IEEE Transactions on Multimedia, 2018, 20(3): 634–644.

[11] [11] Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014: 580–587.

[12] [12] Girshick R. Fast R-CNN[C]//Proceedings of 2015 IEEE International Conference on Computer Vision, 2015: 1440–1448.

[13] [13] Ren S Q, He K M, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[C]//Proceedings of the 28th International Conference on Neural Information Processing Systems, 2015: 91–99.

[14] [14] Shrivastava A, Gupta A, Girshick R. Training region-based object detectors with online hard example mining[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016: 761–769.

[15] [15] Redmon J, Divvala S, Girshick R, et al. You only look once: unified, real-time object detection[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016: 779–788.

[16] [16] Uijlings J R R, Van De Sande K E A, Gevers T, et al. Selective search for object recognition[J]. International Journal of Computer Vision, 2013, 104(2): 154–171.

[17] [17] Zitnick C L, Dollár P. Edge boxes: locating object proposals from edges[C]//Proceedings of the 13th European Conference on Computer Vision, 2014: 391–405.

[18] [18] He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770–778.

[19] [19] Lin T Y, Dollár P, Girshick R, et al. Feature pyramid networks for object detection[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017: 936–944.

[20] [20] Dai W C, Jin L X, Li G N, et al. Real-time airplane detection algorithm in remote-sensing images based on improved YOLOv3[J]. Opto-Electronic Engineering, 2018, 45(12): 180350.

[21] [21] Bodla N, Singh B, Chellappa R, et al. Soft-NMS—improving object detection with one line of code[C]//Proceedings of 2017 IEEE International Conference on Computer Vision, 2017: 5562–5570.

[22] [22] He Y H, Zhang X Y, Savvides M, et al. Softer-NMS: rethinking bounding box regression for accurate object detection[J]. ar-Xiv:1809.08545v1[cs.CV], 2018.