Ten years of pedestrian detection, what have we learned?

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

Volumn 8926, Issue , 2015, Pages 613-627

  Benenson, Rodrigo ^a   Omran, Mohamed ^a   Hosang, Jan ^a   Schiele, Bernt ^a  

^a MAX PLANCK INSTITUTE FOR INFORMATICS   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

FORESTRY; OBJECT DETECTION;

CALTECH; DECISION FOREST; PEDESTRIAN DETECTION;

COMPUTER VISION;

DETECTION; FORESTS; TREES;

EID: 84928818979     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-16181-5_47     Document Type: Conference Paper

References (60)

1. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR (2005)
2. Ess, A., Leibe, B., Schindler, K., Van Gool, L.: A mobile vision system for robust multi-person tracking. In: CVPR. IEEE Press, June 2008
3. Wojek, C., Walk, S., Schiele, B.: Multi-cue onboard pedestrian detection. In: CVPR (2009)
4. Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. PAMI (2009)
5. Keller, C.G., Llorca, D.F., Gavrila, D.M.: Dense stereo-based roi generation for pedestrian detection. In: Denzler, J., Notni, G., Süße, H. (eds.) Pattern Recognition. LNCS, vol. 5748, pp. 81-90. Springer, Heidelberg (2009)
6. Dollar, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: a benchmark. In: CVPR (2009)
7. Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? the kitti vision benchmark suite. In: Conference on Computer Vision and PatternRecognition (CVPR) (2012)
8. Dollár, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: An evaluation of the state of the art. TPAMI (2011)
9. Viola, P., Jones, M.: Robust real-time face detection. IJCV (2004)
10. Sabzmeydani, P., Mori, G.: Detecting pedestrians by learning shapelet features. In: CVPR (2007)
11. Lin, Z., Davis, L.S.: A pose-invariant descriptor for human detection and segmentation. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 423-436. Springer, Heidelberg (2008)
12. Felzenszwalb, P., McAllester, D., Ramanan, D.: A discriminatively trained, multiscale, deformable part model. In: CVPR (2008)
13. Sermanet, P., Kavukcuoglu, K., Chintala, S., LeCun, Y.: Pedestrian detection with unsupervised multi-stage feature learning. In: CVPR (2013)
14. Dollár, P., Tu, Z., Tao, H., Belongie, S.: Feature mining for image classification. In: CVPR (2007)
15. Maji, S., Berg, A., Malik, J.: Classification using intersection kernel support vector machines is efficient. In: CVPR (2008)
16. Wojek, C., Schiele, B.: A performance evaluation of single and multi-feature people detection. In: Rigoll, G. (ed.) DAGM 2008. LNCS, vol. 5096, pp. 82-91. Springer, Heidelberg (2008)
17. Wang, X., Han, X., Yan, S.: An hog-lbp human detector with partial occlusion handling. In: ICCV (2009)
18. Levi, D., Silberstein, S., Bar-Hillel, A.: Fast multiple-part based object detection using kd-ferns. In: CVPR (2013)
19. Felzenszwalb, P., Girshick, R., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. PAMI (2010)
20. Schwartz, W., Kembhavi, A., Harwood, D., Davis, L.S.: Human detection using partial least squares analysis. In: ICCV (2009)
21. Nam, W., Han, B., Han, J.: Improving object localization using macrofeature layout selection. In: ICCV, Visual Surveillance Workshop (2011)
22. Walk, S., Majer, N., Schindler, K., Schiele, B.: New features and insights for pedestrian detection. In: CVPR (2010)
23. Bar-Hillel, A., Levi, D., Krupka, E., Goldberg, C.: Part-based feature synthesis for human detection. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 127-142. Springer, Heidelberg (2010)
24. Paisitkriangkrai, S., Shen, C., van den Hengel, A.: Efficient pedestrian detection by directly optimize the partial area under the roc curve. In: ICCV (2013)
25. Dollár, P., Belongie, S., Perona, P.: The fastest pedestrian detector in the west. In: BMVC (2010)
26. Dollár, P., Tu, Z., Perona, P., Belongie, S.: Integral channel features. In: BMVC (2009)
27. Dollár, P., Appel, R., Kienzle, W.: Crosstalk cascades for frame-rate pedestrian detection. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 645-659. Springer, Heidelberg (2012)
28. Ouyang, W., Wang, X.: A discriminative deep model for pedestrian detection with occlusion handling. In: CVPR (2012)
29. Dollár, P., Appel, R., Belongie, S., Perona, P.: Fast feature pyramids for object detection. PAMI (2014)
30. Marin, J., Vazquez, D., Lopez, A., Amores, J., Leibe, B.: Random forests of local experts for pedestrian detection. In: ICCV (2013)
31. Benenson, R., Mathias, M., Tuytelaars, T., Van Gool, L.: Seeking the strongest rigid detector. In: CVPR (2013)
32. Mathias, M., Benenson, R., Timofte, R., Van Gool, L.: Handling occlusions with franken-classifiers. In: ICCV (2013)
33. Park, D., Ramanan, D., Fowlkes, C.: Multiresolution models for object detection. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 241-254. Springer, Heidelberg (2010)
34. Ouyang, W., Zeng, X., Wang, X.: Modeling mutual visibility relationship with a deep model in pedestrian detection. In: CVPR (2013)
35. Ouyang, W., Wang, X.: Single-pedestrian detection aided by multi-pedestrian detection. In: CVPR (2013)
36. Chen, G., Ding, Y., Xiao, J., Han, T.X.: Detection evolution with multi-order contextual co-occurrence. In: CVPR (2013)
37. Zeng, X., Ouyang, W., Wang, X.: Multi-stage contextual deep learning for pedestrian detection. In: ICCV (2013)
38. Costea, A.D., Nedevschi, S.: Word channel based multiscale pedestrian detection without image resizing and using only one classifier. In: CVPR, June 2014
39. Yan, J., Zhang, X., Lei, Z., Liao, S., Li, S.Z.: Robust multi-resolution pedestrian detection in traffic scenes. In: CVPR (2013)
40. Ouyang, W., Wang, X.: Joint deep learning for pedestrian detection. In: ICCV (2013)
41. Luo, P., Tian, Y., Wang, X., Tang, X.: Switchable deep network for pedestrian detection. In: CVPR (2014)
42. Park, D., Zitnick, C.L., Ramanan, D., Dollár, P.: Exploring weak stabilization for motion feature extraction. In: CVPR (2013)
43. Zhang, S., Bauckhage, C., Cremers, A.B.: Informed haar-like features improve pedestrian detection. In: CVPR (2014)
44. Viola, P., Jones, M., Snow, D.: Detecting pedestrians using patterns of motion and appearance. In: CVPR (2003)
45. Keller, C.G., Enzweiler, M., Rohrbach, M., Fernandez Llorca, D., Schnorr, C., Gavrila, D.M.: The benefits of dense stereo for pedestrian detection. IEEE Transactions on Intelligent Transportation Systems (2011)
46. Ess, A., Leibe, B., Schindler, K., Van Gool, L.: Robust multi-person tracking from a mobile platform. PAMI (2009)
47. Premebida, C., Carreira, J., Batista, J., Nunes, U.: Pedestrian detection combining rgb and dense lidar data. In: IROS (2014)
48. Enzweiler, M., Gavrila, D.: A multilevel mixture-of-experts framework for pedestrian classification. IEEE Transactions on Image Processing (2011)
49. Tu, Z., Bai, X.: Auto-context and its application to high-level vision tasks and 3d brain image segmentation. PAMI (2010)
50. Yan, J., Lei, Z., Wen, L., Li, S.Z.: The fastest deformable part model for object detection. In: CVPR, June 2014
51. Hariharan, B., Zitnick, C.L., Dollár, P.: Detecting objects using deformation dictionaries. In: CVPR (2014)
52. Pedersoli, M., Tuytelaars, T., Gool, L.V.: Using a deformation field model for localizing faces and facial points under weak supervision. In: CVPR, June 2014
53. Benenson, R., Mathias, M., Timofte, R., Van Gool, L.: Pedestrian detection at 100 frames per second. In: CVPR (2012)
54. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: arXiv (2014)
55. Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: Overfeat: integrated recognition, localization and detection using convolutional networks. In: ICLR (2014)
56. Pinheiro, P., Collobert, R.: Recurrent convolutional neural networks for scene labeling. In: JMLR (2014)
57. Azizpour, H., Razavian, A.S., Sullivan, J., Maki, A., Carlsson, S.: From generic to specific deep representations for visual recognition. CoRR (2014)
58. Lim, J., Zitnick, C.L., Dollár, P.: Sketch tokens: a learned mid-level representation for contour and object detection. In: CVPR (2013)
59. Paisitkriangkrai, S., Shen, C., van den Hengel, A.: Strengthening the effectiveness of pedestrian detection with spatially pooled features. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part IV. LNCS, vol. 8692, pp. 546-561. Springer, Heidelberg (2014)
60. Nam, W., Dollár, P., Han, J.H.: Local decorrelation for improved detection. In: Nips (2014)