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 Navneet Dalal and Bill Triggs French National Institute for Research in Computer Science and Control (INRIA) CVPR 05.

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Presentation on theme: " Navneet Dalal and Bill Triggs French National Institute for Research in Computer Science and Control (INRIA) CVPR 05."— Presentation transcript:

1  Navneet Dalal and Bill Triggs French National Institute for Research in Computer Science and Control (INRIA) CVPR 05

2  peopledetect.cpp peopledetect.cpp

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4  Challenge: variable appearance and the wide range of poses  Histogram of Oriented Gradients (HOG) are feature descriptors used in computer vision and image processing for the purpose of object detection. computer visionimage processingobject detection  Basic idea : local object appearance and shape can be characterized rather well by the distribution of local intensity gradients or edge directions.  Similar with edge orientation histograms [4,5], SIFT descriptors [12] and shape contexts [1]

5 64x128

6  INRIA negative images (64x128 samples)

7 Person / non-person classification Person / non-person classification

8  Color / gamma normalization o Grayscale, RGB and LAB color spaces optionally with power law (gamma) equalization o Not obvious effect  Gradient Computation o 1-D point derivatives : uncentred [-1, 1], centred [-1, 0, 1] and cubic-corrected [1,-8, 0, 8,-1] o 3*3 Sobel masks o 2*2 diagonal ones o Gaussian smoothing with σ o 1-D at σ =0 work best o The simplest scheme turns out to be the best DET(Detection Error Tradeoff)

9  Creating the orientation histograms o Weighted vote for an edge orientation histogram over cells. o Unsigned gradients used in conjunction with 9 histogram channels performed best in their human detection experiments o Weight: gradient magnitude itself, or some function of the magnitude (square, square root, clipped) o Gradient magnitude itself generally produces the best results. cell

10  Normalization and descriptor blocks o Owing to local variations of illumination and foreground-background contrast o Group cells into larger, spatially connected blocks and normalize each block separately o Two main block geometries : rectangular R-HOG blocks and circular C-HOG blocks. o R-HOG : 3 parameter # of cells per block # of pixels per cell # of channels per cell histogram Optimal : 3x3 cell blocks of 6x6 pixel cells with 9 channels. Gaussian spatial weight

11  Normalization and descriptor blocks o C-HOG : 4 parameter # of angular bins # of radial bins The radius of the center bin The expansion factor for the radius of additional radial bins Optimal: 4,2,4,2, Gaussian spatial weight is not need o Block Normalization schemes L2-norm : L2-Hys : L2-norm,clip (limit v<=0.2) and renormalize L1-norm : L1-sqrt :

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13  R/C-HOG give near perfect separation on MIT database  Have 1-2 order lower false positives than other descriptors

14  Feed the descriptors into some recognition system :SVM classifier

15 8*8 cell size Histograms of edge orientations edge [-1, 0, 1] gradient filter with no smoothing 8*16 cells 9 unsighted bins=> 9 dimension vector Gaussian spatial window with = 8 R-HOG, 2*2 block size => 36 dimension vector L2-Hys 7*15 blocks => descriptor: 3780 dimension vector overlap=1/2

16  We show experimentally that dense grids of Histograms of Oriented Gradient (HOG) descriptors significantly outperform existing feature sets for human detection.  We study the influence of each stage of the computation on performance.


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