Improving HOG with image segmentation: Application to human detection

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

Volumn 7517 LNCS, Issue , 2012, Pages 178-189

  Socarrás Salas, Yainuvis ^a   Vázquez Bermudez, David ^a   López Peña, Antonio M ^a   Gerónimo Gomez, David ^a   Gevers, Theo ^a,b  

^a UNIVERSITAT AUTÒNOMA DE BARCELONA   (Spain)

^b UNIVERSITY OF AMSTERDAM   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

CANDIDATE GENERATION; DATA SETS; DESCRIPTORS; DETECTION RATES; DETECTION SYSTEM; HIGHER-LEVEL INFORMATION; HUMAN DETECTION; MEAN SHIFT; OBJECT DETECTION; PERFORMANCE IMPROVEMENTS; SEGMENTATION METHODS;

DETECTORS; PIXELS; STATISTICAL TESTS;

IMAGE SEGMENTATION;

EID: 84866427410     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-33140-4_16     Document Type: Conference Paper

References (31)

1. Arbeláez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. IEEE Trans. on Pattern Analysis and Machine Intelligence 99(1), 898-916 (2010)
2. Boix, X., Gonfaus, J., van de Weijer, J., Bagdanov, A., Serrat, J., Gonzàlez, J.: Harmony potentials for joint classification and segmentation. In: IEEE Conf. on Computer Vision and Pattern Recognition, San Francisco, CA, USA (2010)
3. Carreira, J., Sminchisescu, C.: Constrained parametric min-cuts for automatic object segmentation. In: IEEE Conf. on Computer Vision and Pattern Recognition, San Francisco, CA, USA (2010)
4. Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. on Pattern Analysis and Machine Intelligence 24(5), 603-619 (2002)
5. Dalal, N.: Finding people in images and videos. PhD Thesis, Institut National Polytechnique de Grenoble / INRIA Rhône-Alpes (2006)
6. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Conf. on Computer Vision and Pattern Recognition, San Diego, CA, USA (2005)
7. Dollár, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: an evaluation of the state of the art. IEEE Trans. on Pattern Analysis and Machine Intelligence (2011) (in press)
8. Enzweiler, M., Gavrila, D.: Monocular pedestrian detection: survey and experiments. IEEE Trans. on Pattern Analysis and Machine Intelligence 31(12), 2179-2195 (2009)
9. Enzweiler, M., Gavrila, D.: A multi-level mixture-of-experts framework for pedestrian classification. IEEE Trans. on Image Processing (2011) (in press)
10. Everingham, M., van Gool, L., Williams, C., Winn, J., Zisserman, A.: The PASCAL visual object classes (VOC) challenge. Int. Journal on Computer Vision 88(2), 303-338 (2010)
11. Felzenszwalb, P., McAllester, D., Ramanan, D.: A discriminatively trained, multiscale, deformable part model. In: IEEE Conf. on Computer Vision and Pattern Recognition, Anchorage, AK, USA (2008)
12. Gandhi, T., Trivedi, M.: Pedestrian protection systems: issues, survey, and challenges. IEEE Trans. on Intelligent Transportation Systems 8(3), 413-430 (2007)
13. Gavrila, D.: A bayesian, exemplar-based approach to hierarchical shape matching. IEEE Trans. on Pattern Analysis and Machine Intelligence 29(8), 1408-1421 (2007)
14. Gerónimo, D., López, A., Sappa, A., Graf, T.: Survey of pedestrian detection for advanced driver assistance systems. IEEE Trans. on Pattern Analysis and Machine Intelligence 32(7), 1239-1258 (2010)
15. Gould, S., Gao, T., Koller, D.: Region-based segmentation and object detection. In: Advances in Neural Information Processing Systems, Vancouver, BC, Canada (2009)
16. Guo, Z., Zhang, L., Zhang, D.: A completed modeling of local binary pattern operator for texture classification. IEEE Trans. on Pattern Analysis and Machine Intelligence 19(6), 1657-1663 (2010)
17. Jones, M., Snow, D.: Pedestrian detection using boosted features over many frames. In: IEEE Conf. on Computer Vision and Pattern Recognition, Anchorage, AK, USA (2008)
18. Kumar, M., Torr, P., Zisserman, A.: Objcut: Efficient segmentation using top-down and bottom-up cues. IEEE Trans. on Pattern Analysis and Machine Intelligence 32(3), 530-545 (2010)
19. Ladický, Ľ., Sturgess, P., Alahari, K., Russell, C., Torr, P.H.S.:What,Where and HowMany? Combining Object Detectors and CRFs. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 424-437. Springer, Heidelberg (2010)
20. Laptev, I.: Improving object detection with boosted histograms. Image and Vision Computing 27(5), 535-544 (2009)
21. Liao, C.T., Lai, S.H.,Wang,W.H.: A hierarchical image kernel with application to pedestrian identification for video surveillance. In: IEEE Int. Conf. on Image Processing, Cairo, Egypt (2009)
22. Lin, Z., Davis, L.: Shape-based human detection and segmentation via hierarchical part-template matching. IEEE Trans. on Pattern Analysis and Machine Intelligence 32(4), 604-618 (2010)
23. Ott, P., Everingham, M.: Implicit color segmentation features for pedestrain and object detection. In: Int. Conf. on Computer Vision, Kyoto, Japan (2009)
24. Rao, M., Vázquez, D., López, A.: Color contribution to part-based person detection in different types of scenarios. In: International Conference on Computer Analysis of Images and Patterns, Seville, Spain (2011)
25. van de Sande, K., Uijlings, J., Gevers, T., Smeulders, A.: Segmentation as selective search for object recognition. In: Int. Conf. on Computer Vision, Barcelona, Spain (2011)
26. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: IEEE Conf. on Computer Vision and Pattern Recognition, Kauai, HI, USA (2001)
27. Viola, P., Jones, M., Snow, D.: Detecting pedestrians using patterns of motion and appearance. Int. Journal on Computer Vision 63(2), 153-161 (2005)
28. Wang, X., Han, T., Yan, S.: An HOG-LBP human detector with partial occlusion handling. In: Int. Conf. on Computer Vision, Kyoto, Japan (2009)
29. Watanabe, T., Ito, S., Yokoi, K.: Co-Occurrence Histograms of Oriented Gradients for Pedestrian Detection. In: Wada, T., Huang, F., Lin, S. (eds.) PSIVT 2009. LNCS, vol. 5414, pp. 37-47. Springer, Heidelberg (2009)
30. Wyszecki, G., Stiles,W.: Color science: concepts and methods, quantitative data and formulae. Wiley Series in Pure and Applied Optics (1982)
31. Zhang, J., Huang, K., Yu, Y., Tan, T.: Boosted local structured hog-lbp for object localization. In: IEEE Conf. on Computer Vision and Pattern Recognition, Providence, RI, USA (2011)