Histograms of Oriented Gradients for Human Detection

1. Histograms of Oriented Gradients for Human Detection NavneetDalal and Bill Triggs French National Institute for Research in Computer Science and Control (INRIA) CVPR 05

2. OpenCV implement • peopledetect.cpp

4. Introduction • 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.  • 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. Dataset(1/2) 64x128

6. Dataset(2/2) • INRIA negative images (64x128 samples)

7. An overview of our feature extraction and object detection chain Person / non-person classification http://quyanyun.com/Files/Viso/%E7%AC%AC%E5%9B%9B%E8%AE%B2Dalal-phd-slides.pdf

8. Implementation(1/7) • Color / gamma normalization • Grayscale, RGB and LAB color spaces optionally with power law (gamma) equalization • Not obvious effect • Gradient Computation • 1-D point derivatives : uncentred [-1, 1], centred[-1, 0, 1] and cubic-corrected [1,-8, 0, 8,-1] • 3*3 Sobelmasks • 2*2 diagonal ones • Gaussian smoothing with σ • 1-D at σ =0 work best • The simplest scheme turns out to be the best DET(Detection Error Tradeoff)

9. Implementation(2/7) • Creating the orientation histograms • Weighted vote for an edge orientation histogram over cells. • Unsigned gradients used in conjunction with 9 histogram channels performed best in their human detection experiments • Weight: gradient magnitude itself, or some function of the magnitude (square, square root, clipped) • Gradient magnitude itself generally produces the best results. cell

10. Implementation(3/7) • Normalization and descriptor blocks • Owing to local variations of illumination and foreground-background contrast  • Group cells into larger, spatially connected blocks and normalize each block separately • Two main block geometries : rectangular R-HOG blocks and circular C-HOG blocks. • 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. Implementation(4/7) • Normalization and descriptor blocks • 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 • Block Normalization schemes • L2-norm : • L2-Hys : L2-norm ,clip (limit v<=0.2) and renormalize • L1-norm : • L1-sqrt :

12. Implementation(5/7)

13. Implementation(6/7) • R/C-HOG give near perfect separation on MIT database • Have 1-2 order lower false positives than other descriptors

14. Implementation(7/7) • Feed the descriptors into some recognition system :SVM classifier

15. Summary Histograms of edge orientations 8*8 cell size 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*15blocks => descriptor: 3780 dimension vector overlap=1/2

16. Conclusion • 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.