Pedestrian Detection
Histograms of Oriented Gradients for Human Detection
Navneet Dalal and Bill Triggs
Slides from Pete Barnum
Challenges of pedestrian detection
• Wide variety of articulated poses
• Variable appearance/clothing
• Complex backgrounds
• Unconstrined illumination
• Occlusions
• Different Scales
• Histogram of Oriented Gradient descriptor assumes that the local object appearance and
shape within an image can be described by the distribution of intensity gradients or edge
directions.
• The implementation of these descriptors can be achieved by dividing the image into small
connected regions (cells), and for each cell computing a histogram of gradient directions (i.e.
edge orientations) for the pixels within the cell. The combination of these histograms then
represents the descriptor.
• The Histogram of Oriented Gradients descriptor has some key advantages over other
descriptor methods.
– Since it operates on localized cells, it shows invariance to geometric and photometric
transformations such changes (the would only appear in larger spatial regions).
– Coarse spatial sampling, fine orientation sampling, and strong local photometric normalization
permits the individual body movement of pedestrians to be ignored so long as they maintain a
roughly upright position.
–
The HOG descriptor is thus particularly suited for human detection in images.
• Essential in contextually critical environments: surveillance of pedestrians, vehicles,
luggages and groups of unknown objects. Performance limited by
• the occlusion problem often occurring in surveillance applications
• noise occurring in e.g. large illumination variations, persistent shadows
Person detection with HOG descriptors
8 Integral
Images
Sample image
i
Gradient
computation
8 Bins
voting
Concatenation
of 9 HOG descriptors
x(i) = {h1(i),..,h9(i)}
HOG feature vector
h1 h2 h3
h4 h5 h6
h7 h8 h9
HOG h
9 cells
HOG
In the Dalal and Triggs human detection experiment, the optimal parameters were found to be
3x3 cell blocks of 6x6 pixel cells with 9 histogram channels.
• In the Dalal and Triggs experiment tests were performed with different
color spaces:
– RGB
– LAB
– Grayscale
• Gamma Normalization and Compression
– Square root
– Log
• This step can be omitted in HOG descriptor computation, as the descriptor normalization
essentially achieves the same result.
uncentered
centered
cubic-corrected
diagonal
Sobel
• Dalal and Triggs tested several masks, such as the 1-D centered mask, 3x3 Sobel mask or diagonal
masks. The 1-D centered point discrete derivative mask in one of or both the horizontal and vertical
directions (filtering the color or intensity data of the image with the [-1, 0, 1] filter kernel) resulted the
best performance.
• They also experimented Gaussian smoothing before applying the derivative mask, but found that
omission of any smoothing performed better in practice. [
• HOG blocks typically overlap: each cell contributes more than once to the final descriptor.
•Two main block geometries exist.
• rectangular R-HOG blocks
• circular C-HOG blocks
• Some minor improvement in performance can be gained by applying a Gaussian spatial window
within each block before tabulating histogram votes in order to weight pixels around the edge of
the blocks less.
• R-HOG blocks are generally square grids, represented by three parameters:
− the number of cells per block,
− the number of pixels per cell,
− the number of channels per cell histogram.
The R-HOG blocks are different from the scale-invariant feature transform descriptors;
R-HOG blocks are computed in dense grids at some single scale without orientation
alignment, whereas SIFT descriptors are computed at sparse, scale-invariant key image
points and are rotated to align orientation.
The R-HOG blocks are used in conjunction to encode spatial form information, while SIFT
descriptors are used singly.
• C-HOG blocks can be found in two variants: a) With one single, central cell b) With an angularly-
divided central cell. C-HOG blocks can be described with four parameters:
–
the number of angular and radial bins,
–
the radius of the center bin,
–
the expansion factor for the radius of additional radial bins.
C-HOG blocks appear similar to Shape Contexts, but differ strongly in that C-HOG blocks contain
cells with several orientation channels, while Shape Contexts only make use of a single edge
presence count in their formulation.
Histogram of gradient orientations weighted by magnitude
Orientation Position
• Dalal and Triggs found that:
− the two main variants provided equal performance
− two radial bins with four angular bins, a center radius of 4 pixels, and an expansion factor of
2 provided the best performance
− Gaussian weighting provides no benefit when used in conjunction with the C-HOG blocks.
• In their experiments, Dalal and Triggs found the L2-Hys, L2-norm, and L1-sqrt schemes
provide similar performance, while the L1-norm provides slightly less reliable performance.
All four methods showed very significant improvement over the non-normalized data.
• For improved accuracy, the local histograms can be contrast-normalized by calculating a
measure of the intensity across a larger region of the image, called a block, and then using
this value to normalize all cells within the block. This normalization results in better
invariance to changes in illumination or shadowing.
• Dalal and Triggs explored four different methods for block normalization:
− L1-norm
− L2-norm
− L1-sqrt
− L2-Hys
HOG descriptors are fed into a recognition system based on SVM supervised learning which looks
for an optimal hyperplane as a decision function.
In the Dalal and Triggs human recognition tests, they used the freely available SVMLight software
package
Movie example
