# Faster and More Accurate Face

AI and Robotics

Nov 14, 2013 (4 years and 6 months ago)

70 views

Faster and More Accurate Face
Detection on Mobile Robots Using
Geometric Constraints

Michael Dixon, Frederick
Heckel
,

Robert
Pless
, William D. Smart

Washington University in St. Louis

1

Motivation

Why is object detection important?

Examples

Autonomous vehicles

Human
-
robot interaction

2

Object detection in 2D

Common approach

Learn a classifier from labeled examples

Exhaustively classify all subwindows in the image

3

Over 3 million subwindows in a 640
×
480 image

Object detection on a robot

Known camera position and calibration

Knowledge of environment

Goal: use this additional information to
reduce unnecessary tests by the detector

Less computation

Fewer opportunities for false positives

4

Images are projections

Image subwindows correspond to a range of
possible 3D positions and sizes

5

If certain 3D positions and sizes can be ruled out, you
can avoid testing the corresponding subwindows

Geometric constraints

Establish bounds on an object’s physical
position and size

Use the known camera to relate those physical
bounds to the image

Only test a subwindow with the detector if it
satisfies the geometric constraints

6

Face detection on robots

Perfect for geometric constraints

Robot has known camera

People’s heights and sizes are constrained

Height constraint

Size constraint

Known position

Known calibration

7

Applying geometric constraints

For each subwindow,
compute the range of
depths consistent
with the constraints

If the range is empty,
the subwindow can
be safely ignored

8

Satisfies
height
constraint

Satisfies
size
constraint

Height constraints

Given:

height of camera,
h

subwindow

center,
(
u
,
v
)

ground
-
plane normal,
n

height constraints,
h
min

and
h
max

Project
(
u
,
v
)

to ray in
world space,
r

Compute the range of
depths consistent with
the height constraints

9

h

h
max

h
min

n

r

Size constraints

Given:

width of the
subwindow
,
w

the camera’s focal length,
f
x

size constraints,
s
min

and
s
max

Compute the range of
depths consistent with
the size constraints

10

s
max

s
min

r
s

Combining constraints

For each
subwindow
,
(
u,v,w
)
:

Compute range of valid depths,

Can pre
-
compute quickly

Assuming no camera roll,

u

can be ignored

For all
v
, compute
D
h

For all
w
, compute
D
s

Store each
D

in
v

by
w

look
-
up table

Only update if the camera

tilts or zooms

11

D
h
(
v
)

D
s
(
w
)

D

Incorporating depth measurements

For each subwindow,
compute range of depths,
M
, consistent with
external depth
measurements

If
D

and
M

do not
overlap, the subwindow
can be safely ignored

12

Stereo disparity

Incorporating laser range data

into image

Estimate depth at each
pixel assuming a uniform
footprint

Preprocess the range data
to fill small gaps

13

Laser rangefinder

Evaluation

B21r, SICK PLS Laser rangefinder (180 degrees), Bumblebee
stereo camera

Control: Ran OpenCV detector at 25 scales from 20 to 200
pixels, scaling by a factor of 1.1, shifting window in increments
of 0.5∙
w
/
w
0

14

300 pairs of stereo images, 416 labeled faces

Reduction in computation

Geometric constraints
alone cut computation
in half

Incorporating depth
from stereo or laser
reduces computation by
an average of 85%

15

Improved accuracy

Evaluated the
detector over the full
range of sensitivity
thresholds

Three times fewer
false positives

16

Example results

OpenCV

face detector

Geometric constraints + laser measurement

17

Control

Laser