The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Human Detection Based on a Probabilistic Assembly of Robust Part Detectors
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Citations
Cites
Object recognition from local scale-invariant features
Lowe, D. G. (1999) · Proceedings of the Seventh IEEE International Conference on Computer Vision · doi:10.1109/iccv.1999.790410 · Cited by 16,472
A Trainable System for Object Detection
Papageorgiou, C., & Poggio, T. (2000) · International Journal of Computer Vision · doi:10.1023/a:1008162616689 · Cited by 1,289
Research works printed but not linked: 20
- Felzenszwalb, P.: Learning models for object recognition. In: Proc. of the CVPR, Hawaii, USA, pp. 1056–1062 (2001) · DOI
- Felzenszwalb, P., Huttenlocher, D.: Efficient matching of pictorial structures. In: Proc. of the CVPR, Hilton Head Island, USA, pp. 66–75 (2000) · DOI · Paper page
- Fergus, R., Perona, P., Zisserman, A.: Object class recognition by unsupervised scale-invariant learning. In: Proc. of the CVPR, Madison, USA, pp. 264–271 (2003) · DOI · Paper page
- Forsyth, D., Fleck, M.: Body plans. In: Proc. of the CVPR, Puerto Rico, USA, pp. 678–683 (1997) · DOI
- Gavrila, D.M.: Pedestrian detection from a moving vehicle. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 37–49. Springer, Heidelberg (2000) · DOI · Paper page
- Ioffe, S., Forsyth, D.: Probabilistic methods for finding people. International Journal of Computer Vision 43(1), 45–68 (2001) · DOI · Paper page
- Kruppa, H., Schiele, B.: Using local context to improve face detection. In: Proc. of the BMVC, Norwich, England, pp. 3–12 (2003) · DOI
- Li, S., Zhu, L., Zhang, Z., Blake, A., Zhang, H., Shum, H.: Statistical learning of multi-view face detection. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2353, pp. 67–81. Springer, Heidelberg (2002) · DOI · Paper page
- Lindeberg, T.: Detecting salient blob-like image structures and their scales with a scale-space primal sketch - a method for focus-of-attention. International Journal of Computer Vision 11(3), 283–318 (1993) · DOI · Paper page
- Mikolajczyk, K., Choudhury, R., Schmid, C.: Face detection in a video sequence - a temporal approach. In: Proc. of the CVPR, Hawaii, USA, pp. 96–101 (2001) · DOI
- Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. In: Proc. of the CVPR, Madison, USA, pp. 257–263 (2003) · DOI · Paper page
- Mohan, A., Papageorgiou, C., Poggio, T.: Example-based object detection in images by components. IEEE Transactions on PAMI 23(4), 349–361 (2001) · DOI · Paper page
- Shapire, Y.S.R.E.: Improving bossting algorithm using confidence-rated predictions. Machine Learning 37(3), 297–336 (1999) · DOI · Paper page
- Ramanan, D., Forsyth, D.A.: Finding and tracking people from the bottom up. In: Proc. of the CVPR, Madison, USA, pp. 467–474 (2003) · DOI · Paper page
- Ronfard, R., Schmid, C., Triggs, B.: Learning to parse pictures of people. In: Proc. of the ECCV, Copenhagen, Denmark, pp. 700–714 (2002) · DOI · Paper page
- Schneiderman, H.: Learning statistical structure for object detection. In: Proc. of the CAIP, Groningen, Netherlands, pp. 434–441 (2003) · DOI
- Schneiderman, H., Kanade, T.: A statistical method for 3D object detection applied to faces and cars. In: Proc. of the CVPR, Hilton Head Island, USA, pp. 746–751 (2000) · DOI · Paper page
- Sidenbladh, H., Black, M.: Learning image statistics for bayesian tracking. In: Proc. of the ICCV, Vancouver, Canada, pp. 709–716 (2001) · DOI
- Sigal, L., Isard, M., Sigelman, B.H., Black, M.J.: Attractive people: Assembling loose-limbed models using non-parametric belief propagation. In: Proc. of the NIPS, Vancouver, Canada (2003)
- Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proc. of the CVPR, Hawaii, USA, pp. 511–518 (2001) · DOI · Paper page
Cited by
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges
Shakhatreh, H., Sawalmeh, A. H., Al-Fuqaha, A., Dou, Z., Almaita, E., Khalil, I., Othman, N. S., Khreishah, A., & Guizani, M. (2019) · IEEE Access · doi:10.1109/access.2019.2909530 · Cited by 2,529
An HOG-LBP human detector with partial occlusion handling
Wang, X., Han, T. X., & Yan, S. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459207 · Cited by 1,727
Fast Human Detection Using a Cascade of Histograms of Oriented Gradients
Zhu, Q., Yeh, M.-C., Cheng, K.-T., & Avidan, S. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.119 · Cited by 1,399
Pedestrian detection: A benchmark
Dollar, P., Wojek, C., Schiele, B., & Perona, P. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206631 · Cited by 1,313
Metadata
- Authors
- Mikolajczyk, K., Schmid, C., & Zisserman, A.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2004
- Pages
- 69-82
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2156539399
- Cited by
- 608
- References
- 2
- FWCI
- 27.04
- Type
- conference paper
- Language
- en
- Open access
- green
- Licence
- www.springer.com/tdm (tdm)
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- Mikolajczyk, K., Schmid, C., & Zisserman, A. (2004). Human Detection Based on a Probabilistic Assembly of Robust Part Detectors. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-540-24670-1_6
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