Object Detection

• Overview• Viola-Jones• Dalal-Triggs• Deformable models• Deep learning

Recap: Viola-Jones sliding window detector

Fast detection through two mechanisms• Quickly eliminate unlikely windows• Use features that are fast to compute

Viola and Jones. Rapid Object Detection using a Boosted Cascade of Simple Features (2001).

Cascade for Fast Detection

Examples

Stage 1H1(x) > t1?

Reject

No

YesStage 2

H2(x) > t2?Stage N

HN(x) > tN?

Yes

… Pass

Reject

No

Reject

No

• Choose threshold for low false negative rate• Fast classifiers early in cascade• Slow classifiers later, but most examples don’t get there

Features that are fast to compute

• “Haar-like features”– Differences of sums of intensity– Thousands, computed at various positions and

scales within detection window

Two-rectangle features Three-rectangle features Etc.

-1 +1

Integral Images• ii = cumsum(cumsum(im, 1), 2)

x, y

ii(x,y) = Sum of the values in the grey region

SUM within Rectangle D is ii(4) - ii(2) - ii(3) + ii(1)

Feature selection with Adaboost

• Create a large pool of features (180K)• Select features that are discriminative and work well

together– “Weak learner” = feature + threshold + parity

– Choose weak learner that minimizes error on the weighted training set

– Reweight

Viola Jones Results

MIT + CMU face dataset

Speed = 15 FPS (in 2001)

Object Detection

• Overview• Viola-Jones• Dalal-Triggs• Deformable models• Deep learning

Example: Dalal-Triggs pedestrian detector

1. Extract fixed-sized (64x128 pixel) window at each position and scale

2. Compute HOG (histogram of gradient) features within each window

3. Score the window with a linear SVM classifier4. Perform non-maxima suppression to remove

overlapping detections with lower scoresNavneet Dalal and Bill Triggs, Histograms of Oriented Gradients for Human Detection, CVPR05

Slides by Pete Barnum Navneet Dalal and Bill Triggs, Histograms of Oriented Gradients for Human Detection, CVPR05

• Tested with– RGB– LAB– Grayscale

• Gamma Normalization and Compression– Square root– Log

Slightly better performance vs. grayscale

Very slightly better performance vs. no adjustment

uncentered

centered

cubic-corrected

diagonal

Sobel

Slides by Pete Barnum Navneet Dalal and Bill Triggs, Histograms of Oriented Gradients for Human Detection, CVPR05

Outperforms

• Histogram of gradient orientations

– Votes weighted by magnitude– Bilinear interpolation between cells

Orientation: 9 bins (for unsigned angles 0 -180)

Histograms in k x k pixel cells

Slides by Pete Barnum Navneet Dalal and Bill Triggs, Histograms of Oriented Gradients for Human Detection, CVPR05

Normalize with respect to surrounding cells

Slides by Pete Barnum Navneet Dalal and Bill Triggs, Histograms of Oriented Gradients for Human Detection, CVPR05

X=

Slides by Pete Barnum Navneet Dalal and Bill Triggs, Histograms of Oriented Gradients for Human Detection, CVPR05

# features = 15 x 7 x 9 x 4 = 3780

# cells

# orientations

# normalizations by neighboring cells

Original Formulation

Slides by Pete Barnum Navneet Dalal and Bill Triggs, Histograms of Oriented Gradients for Human Detection, CVPR05

pos w neg w

pedestrian

Slides by Pete Barnum Navneet Dalal and Bill Triggs, Histograms of Oriented Gradients for Human Detection, CVPR05

Pedestrian detection with HOG• Train a pedestrian template using a linear support vector

machine• At test time, convolve feature map with template• Find local maxima of response• For multi-scale detection, repeat over multiple levels of a HOG

pyramid

N. Dalal and B. Triggs, Histograms of Oriented Gradients for Human Detection, CVPR 2005

TemplateHOG feature map Detector response map

Something to think about…• Sliding window detectors work

– very well for faces– fairly well for cars and pedestrians– badly for cats and dogs

• Why are some classes easier than others?

Strengths and Weaknesses of Statistical Template Approach

Strengths• Works very well for non-deformable objects with

canonical orientations: faces, cars, pedestrians• Fast detection

Weaknesses• Not so well for highly deformable objects or “stuff”• Not robust to occlusion• Requires lots of training data

Tricks of the trade• Details in feature computation really matter

– E.g., normalization in Dalal-Triggs improves detection rate by 27% at fixed false positive rate

• Template size– Typical choice is size of smallest detectable object

• “Jittering” to create synthetic positive examples– Create slightly rotated, translated, scaled, mirrored versions as

extra positive examples• Bootstrapping to get hard negative examples

1. Randomly sample negative examples2. Train detector3. Sample negative examples that score > -1 4. Repeat until all high-scoring negative examples fit in memory

Things to remember

• Sliding window for search

• Features based on differences of intensity (gradient, wavelet, etc.)– Excellent results require careful feature

design

• Boosting for feature selection

• Integral images, cascade for speed

• Bootstrapping to deal with many, many negative examples

Examples

Stage 1H1(x) >

t1?

Reject

No

YesStage 2H2(x) >

t2?

Stage NHN(x) >

tN?

Yes

…Pass

Reject

No

Reject

No

Many slides from Lana Lazebnik based on P. Felzenszwalb

Generic object detection with deformable part-based models

Challenge: Generic object detection

Histograms of oriented gradients (HOG)• Partition image into blocks at multiple scales and

compute histogram of gradient orientations in each block

N. Dalal and B. Triggs, Histograms of Oriented Gradients for Human Detection, CVPR 2005

10x10 cells

20x20 cells

Image credit: N. Snavely

Histograms of oriented gradients (HOG)• Partition image into blocks at multiple scales and

compute histogram of gradient orientations in each block

N. Dalal and B. Triggs, Histograms of Oriented Gradients for Human Detection, CVPR 2005

Image credit: N. Snavely

Are we done?

• Single rigid template usually not enough to represent a category• Many objects (e.g. humans) are articulated, or

have parts that can vary in configuration

• Many object categories look very different from different viewpoints, or from instance to instance

Slide by N. Snavely

Discriminative part-based models

P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan, Object Detection with Discriminatively Trained Part Based Models, PAMI

32(9), 2010

Root filter

Part filters

Deformation weights

Discriminative part-based models

P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan, Object Detection with Discriminatively Trained Part Based Models, PAMI

32(9), 2010

Multiple components

Discriminative part-based models

P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan, Object Detection with Discriminatively Trained Part Based Models, PAMI

32(9), 2010

Object hypothesis• Multiscale model: the resolution of part

filters is twice the resolution of the root

Scoring an object hypothesis• The score of a hypothesis is the sum of filter scores

minus the sum of deformation costs

),,,()(),...,( 22

0 10 ii

n

i

n

iiiiiin dydxdydxscore

DpHFpp

Filters

Subwindow features

Deformation weights

Displacements

Scoring an object hypothesis• The score of a hypothesis is the sum of filter scores

minus the sum of deformation costs

)()( zHwz score

Concatenation of filter and deformation

weights

Concatenation of subwindow features and displacements

Filters

Subwindow features

Deformation weights

Displacements

),,,()(),...,( 22

0 10 ii

n

i

n

iiiiiin dydxdydxscore

DpHFpp

Detection• Define the score of each root filter location as the

score given the best part placements:

),...,(max)( 0,...,

01

nscorescoren

ppppp

Detection• Define the score of each root filter location as the

score given the best part placements:

• Efficient computation: generalized distance transforms• For each “default” part location, find the score of

the “best” displacement

),,,(),(max),( 22

,dydxdydxdyydxxyxR ii

dydxi DHF

Head filter Deformation cost

),...,(max)( 0,...,

01

nscorescoren

ppppp

Detection• Define the score of each root filter location as the

score given the best part placements:

• Efficient computation: generalized distance transforms• For each “default” part location, find the score of

the “best” displacement

Head filter responsesDistance transformHead filter

),,,(),(max),( 22

,dydxdydxdyydxxyxR ii

dydxi DHF

),...,(max)( 0,...,

01

nscorescoren

ppppp

Detection

Detection result

Training• Training data consists of images with labeled

bounding boxes• Need to learn the filters and deformation parameters

Training• Our classifier has the form

• w are model parameters, z are latent hypotheses

• Latent SVM training:• Initialize w and iterate:

• Fix w and find the best z for each training example (detection)• Fix z and solve for w (standard SVM training)

• Issue: too many negative examples• Do “data mining” to find “hard” negatives

),(max)( zxHwx z f

Car model

Component 1

Component 2

Car detections

Person model

Person detections

Cat model

Cat detections

Bottle model

More detections

Quantitative results (PASCAL 2008)

• 7 systems competed in the 2008 challenge• Out of 20 classes, first place in 7 classes and

second place in 8 classes

Bicycles Person Bird

Proposed approach Proposed approach

Proposed approach

Detection state of the art

Object detection system overview. Our system (1) takes an input image, (2) extracts around 2000 bottom-up region proposals, (3) computes features for each proposal using a large convolutional neural network (CNN), and then (4) classifies each region using class-specific linear SVMs. R-CNN achieves a mean average precision (mAP) of 53.7% on PASCAL VOC 2010. For comparison, Uijlings et al. (2013) report 35.1% mAP using the same region proposals, but with a spatial pyramid and bag-of-visual-words approach. The popular deformable part models perform at 33.4%.

R. Girshick, J. Donahue, T. Darrell, and J. Malik, Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation, CVPR 2014, to appear.