A unified multi-scale deep convolutional neural network for fast object detection

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 9908 LNCS, Issue , 2016, Pages 354-370

  Cai, Zhaowei ^a   Fan, Quanfu ^b   Feris, Rogerio S ^b   Vasconcelos, Nuno ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

Multi scale; Object detection; Unified neural network

Indexed keywords

CONVOLUTIONAL NEURAL NETWORKS; DEEP NEURAL NETWORKS; OBJECT RECOGNITION; SIGNAL SAMPLING;

COMPUTATION COSTS; DETECTION PERFORMANCE; MULTI-SCALE; MULTIPLE OUTPUTS; OBJECT DETECTORS; RECEPTIVE FIELDS; STATE OF THE ART; UNIFIED NEURAL NETWORKS;

OBJECT DETECTION;

EID: 84990050214     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-46493-0_22     Document Type: Conference Paper

References (43)

1. Viola, P.A., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137-154 (2004)
2. Dollár, P., Appel, R., Belongie, S.J., Perona, P.: Fast feature pyramids for object detection. IEEE Trans. Pattern Anal. Mach. Intell. 36(8), 1532-1545 (2014)
3. Girshick, R.B., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: CVPR, pp. 580-587(2014)
4. Girshick, R.B.: Fast R-CNN. In: ICCV, pp. 1440-1448(2015)
5. Chen, X., Kundu, K., Zhu, Y., Berneshawi, A., Ma, H., Fidler, S., Urtasun, R.: 3D object proposals for accurate object class detection. In: NIPS (2015)
6. He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8691, pp. 346-361. Springer, Heidelberg (2014). doi:10.1007/978-3-319-10578-9-23
7. Gidaris, S., Komodakis, N.: Object detection via a multi-region and semantic segmentation-aware CNN model. In: ICCV, pp. 1134-1142(2015)
8. van de Sande, K.E.A., Uijlings, J.R.R., Gevers, T., Smeulders, A.W.M.: Segmentation as selective search for object recognition. In: ICCV, pp. 1879-1886(2011)
9. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards real-time object detection with region proposal networks. In: NIPS (2015)
10. Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? the KITTI vision benchmark suite. In: CVPR, pp. 3354-3361(2012)
11. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR, pp. 3431-3440 (2015)
12. Xie, S., Tu, Z.: Holistically-nested edge detection. In: ICCV, pp. 1395-1403 (2015)
13. Dollár, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: An evaluation of the state of the art. IEEE Trans. Pattern Anal. Mach. Intell. 34(4), 743-761 (2012)
14. Bourdev, L.D., Brandt, J.: Robust object detection via soft cascade. In: CVPR, pp. 236-243 (2005)
15. Cai, Z., Saberian, M.J., Vasconcelos, N.: Learning complexity-aware cascades for deep pedestrian detection. In: ICCV, pp. 3361-3369 (2015)
16. Ohn-Bar, E., Trivedi, M.M.: Learning to detect vehicles by clustering appearance patterns. IEEE Trans. Intell. Transp. Syst. 16(5), 2511-2521 (2015)
17. Saberian, M.J., Vasconcelos, N.: Boosting algorithms for detector cascade learning. J. Mach. Learn. Res. 15(1), 2569-2605 (2014)
18. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS, pp. 1106-1114(2012)
19. Felzenszwalb, P.F., Girshick, R.B., McAllester, D.A., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627-1645 (2010)
20. Wang, X., Yang, M., Zhu, S., Lin, Y.: Regionlets for generic object detection. In: ICCV, pp. 17-24 (2013)
21. Redmon, J., Divvala, S.K., Girshick, R.B., Farhadi, A.: You only look once: Unified, real-time object detection. In: CVPR (2016)
22. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: CVPR, pp. 1-9 (2015)
23. Lee, C., Xie, S., Gallagher, P.W., Zhang, Z., Tu, Z.: Deeply-supervised nets. In: AISTATS (2015)
24. Benenson, R., Mathias, M., Timofte, R., Gool, L.J.V.: Pedestrian detection at 100 frames per second. In: CVPR, pp. 2903-2910 (2012)
25. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556 (2014)
26. Bell, S., Zitnick, C.L., Bala, K., Girshick, R.B.: Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks. In: CVPR (2016)
27. Zhu, Y., Urtasun, R., Salakhutdinov, R., Fidler, S.: segDeepM: Exploiting segmentation and context in deep neural networks for object detection. In: CVPR, pp. 4703-4711 (2015)
28. Everingham, M., Gool, L.J.V., Williams, C.K.I., Winn, J.M., Zisserman, A.: The pascal visual object classes (VOC) challenge. Int. J. Comput. Vis. 88(2), 303-338 (2010)
29. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M.S., Berg, A.C., Li, F.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211-252 (2015)
30. Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R.B., Guadarrama, S., Darrell, T.: Caffe: Convolutional architecture for fast feature embedding. In: MM, pp. 675-678 (2014)
31. Hosang, J., Benenson, R., Dollár, P., Schiele, B.: What makes for effective detection proposals? PAMI 38(4), 814-830 (2015)
32. Cheng, M., Zhang, Z., Lin, W., Torr, P.H.S.: BING: binarized normed gradients for objectness estimation at 300fps. In: CVPR, pp. 3286-3293 (2014)
33. Zitnick, C.L., Dollár, P.: Edge boxes: locating object proposals from edges. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 391-405. Springer, Heidelberg (2014). doi:10.1007/978-3-319-10602-1-26
34. Arbeláez, P.A., Pont-Tuset, J., Barron, J.T., Marqués, F., Malik, J.: Multiscale combinatorial grouping. In: CVPR, pp. 328-335 (2014)
35. Geiger, A., Wojek, C., Urtasun, R.: Joint 3D estimation of objects and scene layout. In: NIPS, pp. 1467-1475 (2011)
36. Pepik, B., Stark, M., Gehler, P.V., Schiele, B.: Multi-view and 3D deformable part models. IEEE Trans. Pattern Anal. Mach. Intell. 37(11), 2232-2245 (2015)
37. Xiang, Y., Choi, W., Lin, Y., Savarese, S.: Data-driven 3D voxel patterns for object category recognition. In: CVPR, pp. 1903-1911 (2015)
38. Li, B., Wu, T., Zhu, S.-C.: Integrating context and occlusion for car detection by hierarchical and-or model. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8694, pp. 652-667. Springer, Heidelberg (2014). doi:10.1007/978-3-319-10599-4-42
39. Tian, Y., Luo, P., Wang, X., Tang, X.: Deep learning strong parts for pedestrian detection. In: ICCV, pp. 1904-1912 (2015)
40. Zhang, S., Benenson, R., Schiele, B.: Filtered channel features for pedestrian detection. In: CVPR, pp. 1751-1760 (2015)
41. Paisitkriangkrai, S., Shen, C., van den Hengel, A.: Pedestrian detection with spatially pooled features and structured ensemble learning. CoRR abs/1409.5209 (2014)
42. Yang, F., Choi, W., Lin, Y.: Exploit all the layers: Fast and accurate CNN object detector with scale dependent pooling and cascaded rejection classifiers. In: CVPR. (2016)
43. Nam, W., Dollár, P., Han, J.H.: Local decorrelation for improved pedestrian detection. In: NIPS, pp. 424-432 (2014)