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An HOG-CT human detector with histogram-based search

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Abstract

This paper addresses the problem of human detection in still images. We first describe a novel descriptor concatenating the local normalized histogram of oriented gradients (HOG) and the global normalized histogram of census transform (CT) of images for human detection. The detector is trained by using cascade learning method based on AdaBoost. In addition, we propose an easy histogram-based search method, termed the block histogram, which can reduce the computational cost and speed up the process of detection when sliding in the test image. Experimental results on the INRIA person dataset show that the proposed method can achieve competitive results both in discriminating power and detection speed as compared to the state-of-the-art.

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References

  1. An S, Peursum P, Liu W, Venkatesh S (2009) Efficient algorithms for subwindow search in object detection and localization. In: CVPR

  2. Begard J, Allezard N, Sayd P (2008) Real-time human detection in urban scenes: local descriptors and classifiers selection with adaboost-like algorithms. In: CVPR Workshops

  3. Chang HW, Chen HT (2010) A square-root sampling approach to fast histogram-based Search. In: CVPR

  4. Chen YT, Chen CS (2008) Fast human detection using a novel boosted cascading structure with meta stages. IEEE Trans Image Process 17(8):1452–1464

    Article  MathSciNet  Google Scholar 

  5. Chum O, Zisserman A (2007) An exemplar model for learning object classes. In: CVPR

  6. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: CVPR, San Diego, CA. Volume I., 886–893

  7. Enzweiler M, Gavrila D (2009) Monocular pedestrian detection: survey and experiments. IEEE Trans Pattern Anal Mach Intell 31(12):2179–2195

    Article  Google Scholar 

  8. Geronimo D, Lopez AM, Sappa AD, Graf T (2010) Survey of pedestrian detection for advanced driver assistance systems. IEEE Trans Pattern Anal Mach Intell 32(7):1239–1258

    Article  Google Scholar 

  9. Harzallah H, Jurie F, Schmid C (2009) Combining efficient object localization and image classification. In: ICCV

  10. Huang X, Li S, Wang Y (2004) Shape localization based on statistical method using extended local binary pattern. International Conference on Image and Graphics, Hong Kong, China, 184–187

  11. Huang T, Yang G, Tang G (1979) A fast two-dimensional median filtering algorithm. ICASSP 27(1):13–18

    Google Scholar 

  12. Lampert CH (2009) Detecting objects in large image collections and videos by efficient subimage retrieval. In: ICCV

  13. Lampert CH, Blaschko MB, Hofmann T (2008) Beyond sliding windows: object localization by efficient subwindow search. In: CVPR

  14. Lin Z, Davis LS (2008) A pose-invariant descriptor for human detection and segmentation. 10th European Conference on Computer Vision, Marseille, France

  15. Liu Y, Shan S, Zhang W, Chen X, Gao W (2009) Granularity-tunable gradients partition (GGP) descriptors for human detection. IEEE Comput Soc Int Conf Comput Vis Pattern Recognit, CVPR

  16. Lowe D (2004) Distinctive image features from scale-invariant keypoints. IJCV 60(2):91–110

    Article  Google Scholar 

  17. Maji S, Berg A (2009) Max-margin additive classifiers for detection. In: ICCV

  18. Maji S, Berg A, Malik J (2008) Classification using intersection kernel SVMs is efficient. In: CVPR

  19. Mu Y, Yan S, Liu Y, Huang T, Zhou B (2008) Discriminative local binary patterns for human detection in personal album. In: CVPR, 1–8, June

  20. Ojala T, Pietikäinen M, Harwood D (1996) A comparative study of texture measures with classification based on feature distributions. Pattern Recogn 29:51–59

    Article  Google Scholar 

  21. Ott P, Everingham M (2009) Implicit color segmentation features for pedestrian detection. In: ICCV

  22. Sabzmeydani P, Mori G (2007) Detecting pedestrians by learning shapelet features. In: CVPR

  23. Schuldt C, Laptev I, Caputo B (2004) Recognizing human actions: a local SVM approach. In: ICPR

  24. Schwartz WR, Kembhavi A, Harwood D, Davis LS (2009) Human detection using partial least squares analysis. In: ICCV

  25. Sizintsev M, Derpanis KG, Hogue A (2008) Histogram-based search: a comparative study. In: CVPR

  26. Tuzel O, Porikli F, Meer P (2007) Human detection via classification on riemannian manifolds. In: CVPR

  27. Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. In: CVPR

  28. Wang X, Han TX, Yan S (2009) An HOG-LBP human detector with partial occlusion handling. In: ICCV

  29. Watanabe T, Ito S, Yokoi K (2010) Co-occurrence histograms of oriented gradients for human detection. Inform Media Tech 5(2):659–667

    Google Scholar 

  30. Wei Y, Tao L (2010) Efficient histogram-based sliding window. In: CVPR

  31. Wojek C, Schiele B (2008) A performance evaluation of single and multi-feature people detection. In: DAGM

  32. Wojek C, Walk S, Schiele B (2009) Multi-cue onboard pedestrian detection. In: CVPR

  33. Wu J, Rehg JM (2011) CENTRIST: A visual descriptor for scene categorization. IEEE Trans. Pattern Anal. Mach. Intell., Vol. 33, Nr. 8, pp. 1489–1501

    Google Scholar 

  34. Wu B, Nevatia R (2005) Detection of multiple, partially occluded humans in a single image by Bayesian combination of Edgelet part detectors. In: ICCV

  35. Wu B, Nevatia R (2008) Optimizing discrimination-efficiency tradeoff in integrating heterogeneous local features for object detection. In: CVPR

  36. Wu J, Rehg JM (2008) Where am I: place instance and category recognition using spatial PACT. In: CVPR, Anchorage, Alaska, June 24–26

  37. Xu R, Zhang B, Ye Q, Jiao JB (2010) Cascaded L1-norm Minimization Learning (CLML) classifier for human detection. In: CVPR

  38. Zabih R, Woodfill J (1996) A non-parametric approach to visual correspondence. IEEE Trans Pattern Anal Mach Intell

  39. Zhang W, Zelinsky G, Samaras D (2007) Real-time accurate object detection using multiple resolutions. In: ICCV

  40. Zhu Q, Yeh MC, Cheng KT, Avidan S (2006) Fast human detection using a cascade of histograms of oriented gradients. In: CVPR, 2006, pages 1491–1498

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Authors and Affiliations

  1. State Key Laboratory of CAD & CG, Zhejiang University, Hangzhou, China

    Jianhao Ding & Weidong Geng

  2. Institute of Computer graphics and Image Processing, Hangzhou Dianzi University, Hangzhou, China

    Jianhao Ding & Yigang Wang

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  1. Jianhao Ding
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  2. Yigang Wang
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  3. Weidong Geng
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Correspondence to Jianhao Ding.

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Ding, J., Wang, Y. & Geng, W. An HOG-CT human detector with histogram-based search. Multimed Tools Appl 63, 791–807 (2013). https://doi.org/10.1007/s11042-011-0896-9

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  • DOI: https://doi.org/10.1007/s11042-011-0896-9

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