Faster and More Accurate Face

bouncerarcheryΤεχνίτη Νοημοσύνη και Ρομποτική

14 Νοε 2013 (πριν από 3 χρόνια και 9 μήνες)

59 εμφανίσεις

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


Advantages of robot vision


Known camera position and calibration


Knowledge of environment


Additional sensors


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


Project laser readings
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