Feature Evaluation and Management

for Camera Pose Tracking on 3D Models

Martin Schumann, Jan Hoppenheit and Stefan M

uller

Institute of Computational Visualistics, University of Koblenz, Koblenz, Germany

Keywords:

Camera, Pose, Tracking, Model, Feature, Evaluation, Management.

Abstract:

Our tracking approach uses feature evaluation and management to estimate the camera pose on the camera

image and a given geometric model. The aim is to gain a minimal but qualitative set of 2D image line and 3D

model edge correspondences to improve accuracy and computation time. Reducing the amount of feature data

makes it possible to use any complex model for tracking. Additionally, the presence of a 3D model delivers

useful information to predict reliable features which can be matched in the camera image with high probability

avoiding possible false matches. Therefore, a quality measure is deﬁned to evaluate and select features best

ﬁtted for tracking upon criteria from rendering process and knowledge about the environment like geometry

and topology, perspective projection, light and matching success feedback. We test the feature management to

analyze the importance and inﬂuence of each quality criterion on the tracking and to ﬁnd an optimal weighting.

1 INTRODUCTION

Many approaches in markerless tracking derive the

pose of the camera - the viewing position and ori-

entation - by using corresponding 2D-2D features in

succeeding frames of the video input. Regarding im-

age information only for detection and description of

features, correspondences become ambiguous. Dis-

turbing lighting conditions and occlusion may cause

unstable results. Further, sequential frame-to-frame

tracking is prone to accumulate detection and match-

ing errors which leads to drift in the camera pose.

In model-based tracking the scene is represented

by 3D data which can be available from a modeling

process or is created online while tracking. The cam-

era pose is derived from minimization of the distance

error between the projections of the 3D model fea-

tures and strong gradients in the image representing

their 2D correspondences. Tracking on a CAD model

was shown by (Comport et al., 2003) describing com-

plex structures of the tracked object. Respectable suc-

cess in tracking a line model in combination with

point features could be demonstrated by (Vacchetti

et al., 2004) and (Wuest and Stricker, 2007) used the

depth buffer to extract contour lines of a 3D model

for tracking. Recently, (Oikawa et al., 2012) applied

local quadric approximations of the object contour to

minimize the distance between model and camera im-

age.

The beneﬁt of a 3D model is the additional in-

formation which can be used to predict image fea-

tures suitable for robust tracking. In the ”analysis-by-

synthesis” approach, a synthetic image of the given

model is rendered as a reference to the camera image,

based on the last veriﬁed camera pose. The properties

of model, camera and real environment are used as

quality criteria for selecting only the most stable fea-

tures in every frame. The idea is a feature manager

that creates a feature list with annotated descriptor

vectors deﬁning a quality measure to evaluate the fea-

tures. Selecting a minimal but qualitative set of fea-

tures not only reduces computation time signiﬁcantly,

but may also improve the tracking accuracy by ﬁlter-

ing unstable features leading to bad correspondences.

Management of point features has been done

successfully on sequential frame-to-frame tracking

by (Shi and Tomasi, 1994). The quality of image fea-

tures is evaluated regarding the change of the feature

appearance in the image and by detection of occlu-

sions and noise. The feature in the current frame is

compared to its ﬁrst detection in the sequence. A

high dissimilarity shows that the feature has under-

gone a strong change which leads to a discard of the

feature. However, the quality of the features is only

judged by image intensities as most feature detec-

tors do. Another approach to select optimal image

features is shown in (Collins et al., 2005). The fea-

tures are evaluated and ranked based on their qual-

562

Schumann M., Hoppenheit J. and Müller S..

Feature Evaluation and Management for Camera Pose Tracking on 3D Models.

DOI: 10.5220/0004685905620569

In Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISAPP-2014), pages 562-569

ISBN: 978-989-758-009-3

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

3D-Model Camera Image

Geometry List +

Quality Descriptor

with History

Initial geometry

preprocessing

Feedback

Pose

Estimator selects

Features

Rendering

Projection

Matching

Rendered Image

Light + Material

Information

Camera pose

Update

Visibility Test

Figure 1: Feature management: Initial preprocessing of the 3D model data creates a geometry list. 1. Each feature is annotated

by a quality descriptor vector. A weighted quality value is calculated and a subset of the geometry is selected for the following

visibility test. 2. The matcher gives feedback about the matching result for the projected geometry. A reﬁned quality value is

calculated. 3. The pose estimator selects the best features from the ranked input list.

ity to discriminate between object and background.

In (Wuest et al., 2007) features are managed regarding

their tracking probability from certain reconstructed

camera positions. Only those features will be used

which are likely to be visible from a given camera po-

sition. Features without valid reconstructed camera

position are removed. A conﬁdence measure based on

texture analysis is proposed by (Steffens et al., 2009)

for stereo matching. Here, false correspondences are

reduced by prediction of regions reliable for match-

ing. Further, (Choi and Christensen, 2010) identify

sharp edge features using the face-normal information

of a model in a combined edge and keypoint tracking

framework.

2 FEATURE EVALUATION AND

MANAGEMENT

2.1 Overview

Unmanaged markerless tracking without quality eval-

uation usually considers all features detected in an

image for pose estimation. It is assumed that us-

ing a high count of features delivers enough good

correspondences to reduce the inﬂuence of erroneous

matches. This may lead to inaccuracies and to higher

computational load. In the presence of a model, the

additional knowledge of geometry and environment

can be used to identify suitable features which re-

duces the amount of data and may lead to a higher

quality of the correspondences.

Figure 1 gives an overview of the feature manage-

ment. The 3D model holds the complete geometry

data as a wireframe of edges. After initial preprocess-

ing a geometry list of model edge features is built. In

the ﬁrst step each feature in the geometry list is anno-

tated by a quality descriptor vector deﬁning a measure

for evaluation. The descriptor contains quality criteria

of model and camera-dependent information, as well

as light and material input and additionally holds a

history value that describes the evolution of the fea-

ture quality over the last frames. The values are in the

range [0,1] or have binary values - e.g. a feature can

be a silhouette (= 1) or it is not (= 0). From all in-

formation collected, a combined quality value in the

range of [0, 1] is calculated for each feature, where 1.0

denotes the best quality.

In the second step, a ﬁltered subset of the geome-

try holding a minimum quality is rendered from the

current camera pose and the geometry features are

checked for visibility. The list of visible geometry

together with the annotated descriptors is sent to the

matcher. 2D image correspondences are detected and

feedback about the matching result is given to the de-

scriptor to reﬁne the quality value of each edge. The

history value is updated and the pose estimator now

selects the best features from the resulting ranked in-

put list for estimation of the new pose. Deﬁning a

value from several quality criteria makes it possible

to dynamically rank the features, so the pose estima-

FeatureEvaluationandManagementforCameraPoseTrackingon3DModels

563

tor can request features with highest priority. If there

are not enough features for successful pose estima-

tion, features of lower priority can be added. In the

following sections we describe the criteria for deﬁn-

ing the quality descriptor in detail.

2.2 Preprocessing

When loading the model, an enhanced winged-edge

list is built, containing the whole wireframe informa-

tion with access to neighboring faces of each edge. If

both faces are planar or the connecting angle is below

a threshold, the edge will have no or only weak dif-

ferences of intensity under lighting and thus may hard

to be recognized by image processing. Regarding the

orientation of the neighboring faces by the angle be-

tween the face normals, edges connecting ﬂat areas

can be removed from the wireframe for simpliﬁcation

of the model data. Additionally, line segments with

common start and end points aligned in equal direc-

tion are merged to one edge feature forming a straight

visible line. Merging reduces the amount of data and

the computational load accordingly.

Further, parallelism is tested by comparing the

direction of the geometry edges. Parallel ones, i.e.

the dot product is approximately 1.0 or −1.0, are as-

signed to a common group. This is neccessary for

later quality evaluation of the distance. In preparation

of the visibility test and hidden line removal, a list of

possible occluders is deﬁned for all edges. The faces

of the model are assigned unique material colors in

the red channel. Each edge now holds a list of colors

for all faces not connected to this edge, since these

faces may cause an occlusion. Optionally, thinning

the model may be reasonable when it is very detailed,

e.g. the windows consist of more geometry than the

visible outline (Fig. 2). Therefore, very short and par-

allel edges can be removed to lower the redundancy

in the data structure. The remaining features after the

initial face angle test and merging are saved in the ge-

ometry list.

2.3 Visibility Test

For every frame each edge in the preprocessed geom-

etry list is ﬁrst tested for the orientation of its neigh-

boring faces in relation to the camera. Edges connect-

ing backfaces are not regarded for the current frame.

Second, all edges connecting two frontfacing poly-

gons or one front- and one backfacing polygon are

checked for occlusions. This is done by rendering the

faces with the material color into a color-index tex-

ture. From the preprocessing step a list of possible

occluders is known for each edge. The edges are pro-

Figure 2: Model preprocessing. Top: Full wireframe (left)

and after face test (right). Bottom: Result after thinning.

Figure 3: Visibility test. Top: Color-index texture (left)

and preprocessed edges (right). Bottom: Resulting visible

edges.

jected and for the pixel positions along the resulting

image line, the color is read from the index texture. If

the retrieved color is not in the list of occluders, a vis-

ibility count for the edge is incremented. An edge is

considered visible when at least a minimal resolution-

dependent number of pixels is not occluded (Fig. 3).

2.4 Distance

Feature edges with small distance in world or image

space are difﬁcult to distinguish when they are paral-

lel and may lead to erroneous correspondences while

matching. The quality value of such edges should be

decreased. Again, this criterion can be deﬁned as per-

spectively dependent distance in relation to the cam-

era position. All edges that have been assigned to one

common group of parallel edges in the preprocessing

step are now checked for their distance. This is done

by deﬁning the two planes spanned between the 3D

edges and the camera center (Fig. 4). The cross prod-

ucts between the vectors from the start points s

, s

of the edges to the camera center C, and the direc-

tion vectors of the edges result in the normal vectors

, n

of the planes. The angle between both plane

VISAPP2014-InternationalConferenceonComputerVisionTheoryandApplications

564

Figure 4: Distance measured by angle between edge-

camera planes.

Figure 5: Length measured by camera angle between start

and end point.

normals corresponds to the angular aperture of both

planes from the camera view and is stored as distance

quality criterion q

distance

= (e

− s

) × (C − s

)

= (e

− s

) × (C − s

)

distance

8 ∗ arccos(

·~n

|~n

|·|~n

)

The principle is that smaller distances between

two edges in 3D space lead to higher parallelism

of their projections in 2D, whereas higher 3D dis-

tances result in 2D lines converging in one vanishing

point. The optimal edge distance is deﬁned by 1/8 of

the image height. Edges with lower distance are re-

garded being ambiguous while matching because of

their parallel alignment. Distances above this value

are clipped to 1, so the entry of the distance criterion

for the quality descriptor will be in the range [0,1].

2.5 Length

Lines become more signiﬁcant with growing length,

promising higher stability during search for corre-

spondences. It would be possible to consider the ab-

solute length in 3D coordinates or the 2D pixel length

of the feature in the image after projection. But it is

more meaningful to evaluate the perspectively depen-

dent length in relation to the camera position as fol-

lows. The angular aperture of the camera to the edge

is considered as quality criterion q

length

(Fig. 5). From

the camera center C two vectors ~v

, ~v

are spanned to

the start point s and end point e of the feature edge and

θ is the horizontal aperture of the camera in radians:

s −C

|s −C|

e −C

|e −C|

length

2 ∗ arccos(~v

·~v

)

The angle gets small when the edge is distant from

the camera or when the direction of the edge is nearly

aligned in the viewing direction. The optimal edge

length after projection is deﬁned by the half image

height. Values above are clipped to 1, so the entry of

the length criterion in the quality descriptor is in the

range [0,1].

2.6 Position

The position of the feature in world coordinates can

give a clue about the expected occurrance of noise.

When tracking real buildings in an outdoor scenario,

a small height above ground leads to the presumption

that frequent occlusions may occur, caused by mov-

ing objects or persons as well as by vegetation. It is

conceivable to ﬁlter those features with a height be-

low a threshold because of unreliable detection in the

image. This may be realized by an additional quality

criterion for static exclusion of the feature. A more

ﬂexible solution would be a dynamic evaluation of its

detection reliability. This is realized by regarding the

matching success feedback and the history value of

the feature as described below.

2.7 Direction

Edges are most stable features when they lie in a pla-

nar view to the image plane, i.e. no signiﬁcant change

in the depth value between the start and end point oc-

curs. This fact can be expressed by the dot product

between the viewing direction ~v of the camera and the

direction vector of the edge

d from start point s to end

point e. A perpendicular alignment is best, thus the

inverse result of the dot product is stored as quality

criterion q

direction

d =

e − s

|e − s|

direction

= 1 − |~v ·

d|.

FeatureEvaluationandManagementforCameraPoseTrackingon3DModels

565

2.8 Silhouette

One may assume that model edges forming the outer

silhouette of an object are good features to separate

the model from the background. Especially in scenar-

ios with buildings they stick out when viewed against

the sky. With the normals ~n

, ~n

representing the ori-

entation of the neighboring faces of each edge and the

view vector ~v

between edge and camera, it can eas-

ily be tested by the dot products, if one of the faces

is aligned towards the camera and one is facing away,

creating a silhouette feature. If this is the case, the

feature quality should be raised by setting the silhou-

ette criterion entry to 1:

silhouette

(

1 if (~v

· ~n

)(~v

· ~n

) ≤ 0,

0 else.

2.9 Light and Material

The lighting situation has a strong impact on the abil-

ity to detect edge features in the image. If two neigh-

boring faces of an edge facing towards the camera are

illuminated by the same intensity, image processing

methods will have a hard time recognizing the gradi-

ent of the image line (Fig. 6). Thus, when the position

of the light source is known, regarding the dot prod-

ucts between the light vectors

pointing from the

faces to the light and the face normals ~n

, ~n

, the dif-

ference of the neighboring dot products describes the

lightning quality:

light

= |(

· ~n

) − (

· ~n

)|.

Also regarding the materials of the model may give

a hint about the probability a feature can be detected

reliably. If the material has bad properties like high

reﬂectance values (i.e. glass, windows), an additional

material entry can be set to 0, reducing the overall

quality.

Figure 6: Example for difﬁcult and optimal light situation.

2.10 Matching-Feedback

The matching process establishes correspondences

between the projected model edges and image lines

which form the input for pose estimation. Knowledge

from model and rendering process can help to deﬁne

a criterion of matching success and quality. After pro-

jection of the geometry edges we know about the ex-

pected pixel length of the image line that the matcher

should be able to ﬁnd in the image. If the machter

returns the number of pixels it was actually able to

detect for the corresponding image line, the relation

between expectation and matching retrieval describes

the quality of the match in the range [0, 1]. In the

optimal case the length of the image line correspon-

dence meets the expected length, resulting in a match

quality ratio of 1. If the found image line does not

fulﬁll a minimal resolution-dependent pixel length it

is rejected. For realizing this, we use a shader-based

matcher that operates on a canny-ﬁltered camera im-

age and counts the number of pixels of the corre-

sponding image line match for each projected feature

edge.

2.11 Conﬁguration

After establishing correspondences from the proposed

features, a further analysis of the geometrical conﬁg-

Figure 7: Visualization of some quality criteria from two

views. Top to bottom: Length, distance, silhouette, direc-

tion, light. Rating between [0,1] from red to green.

VISAPP2014-InternationalConferenceonComputerVisionTheoryandApplications

566

uration can be helpful. The pose estimator demands

certain requirements concerning known critical con-

ﬁgurations, not contributing to the information for the

pose or causing ambiguous results. Regarding dis-

tances and angles between planes and lines, these cor-

respondences can be marked for rejection.

For straight lines ill collinear conﬁgurations oc-

cur if three or more lines are parallel or if three or

more lines intersect in one point. Hence, other fea-

tures should be prioritized for the pose estimation

process. Parallelism among the correspondences is

checked by calculating the dot product of the nor-

malized direction vectors. If all products are close

to 1, the correspondences are rejected. To check for a

common point of three lines, the intersection point of

each correspondence pair is calculated respectively.

If there are two intersection points with an euclidean

distance below a predeﬁned threshold, the line corre-

spondences are rejected.

2.12 History

For evaluating the quality of a feature it is also rea-

sonable regarding the evolution of the feature quality.

A feature is particularly suitable for tracking when it

could be tracked stable over time, i.e. its history val-

ues are high and show little variance. We use the re-

sults of the last 10 frames to describe the history of the

feature by storing its quality value in an array which

is updated every frame. All quality values stored in

the history over the last frames are combined to one

average history value, that contributes to the compu-

tation of the current quality descriptor for the edge.

Hence, the feature management is able to learn about

features which proved unstable in the past (e.g. due to

occlusion) and thus will not be proposed for tracking

in the following frames. When starting the tracking,

initially the history value is empty and thus ignored as

quality criterion.

2.13 Quality Calculation

After all information is collected, an overall quality

value is calculated for each feature in two steps. Be-

fore projection, the weighted average from the entries

of the quality descriptor vector is taken to retrieve a

quality value in the range of [0,1]. This is realized by

the dot product of the descriptor vector Q with qual-

ity criteria q

1..n

and a weight vector W with weight

entries w

1..n

. The result is normalized by the sum of

all weights:

Q =







...







W =







...







overall

(

Q) · (

W )

∑

i=1

An adequate weighting of the criteria according to

their inﬂuence on the tracking result has to be found

during the tests in the next section. A subset of only

those features holding a minimum quality will be pro-

jected and tested for visibility. This ﬁrst ﬁltering leads

to a considerable reduction in computation time. Af-

ter the subsequent matching process, the quality value

calculated before is now reﬁned with the weighted

matching feedback in an additional step. Each fea-

ture in the geometry list is now assigned to its quality

value and the list can be sorted into priority classes,

using predeﬁned boundary values. From this prior-

itized geometry list, the pose estimator is then able

to select a minimal qualitative subset of correspon-

dences.

3 RESULTS

The feature management concept is tested on syn-

thetic rendered and real camera images of simple and

complex objects on indoor and outdoor scenes with

varying lighting conditions. The image resolution of

the input streams is 640 x 480 and 1280 x 720 pix-

els. The initial camera pose is known at the start of

the sequence and the intrinsic parameters of the video

camera are given. The rotation error is measured in

degrees and the translation error is measured in object

units (dimension of the object is 2). For the compu-

tation of the camera pose from line correspondences

we use a non-linear Levenberg-Marquardt optimiza-

tion. The test is accomplished both using a full feature

set without evaluation and using a reduced set with

our feature management. Nearly 2200 test passes are

performed on two video sequences in order to deter-

mine the inﬂuence of the individual quality criteria

on the tracking result. In the test passes all possible

combinations of the quality criteria are used for the

calculation of the quality values while varying the ac-

cording weights. For each pass the average translation

and rotation error is compared to retrieve the optimal

weighting vector leading to a robust and fast camera

pose estimation. The resulting weights are listed in

table 1.

We found the history criterion as well as length,

distance and silhouette have a major impact on the

selection of good features, independently from the

scene content. Thus, in most cases the edges pro-

posed by the feature management are part of the ob-

ject silhouette as can be seen in ﬁgure 8. If additional

knowledge about the light source position is available,

FeatureEvaluationandManagementforCameraPoseTrackingon3DModels

567

Table 1: Optimal weighting of the quality criteria.

Length 3

Distance 3

Silhouette 3

Direction 1

Light + Material 3

History 4

Matching-Feedback 2

the light quality criterion is of equal importance, but

never gains more inﬂuence than the other criteria. A

positive effect of preferring structures aligned planar

to the camera could not be veriﬁed. The history cri-

terion is strongly scene dependent. Especially when

there are few prominent large edges and a high rate

of occlusion, a higher weighting of the history entry

improves the tracking result. This is because the fea-

ture management is learning quickly about these un-

stable features and suppresses them. In a scene with

long unambiguous edges a history weight of 2 proved

sufﬁcient for stable feature selection. As was con-

ceivable, the feedback about the matching result is the

most important criterion for a meaningful prediction

of the overall feature quality. This fact is considered

by double weighting the matching quality result on

the average of all other criteria in an additional step.

The results also show that the weighting of some qual-

ity criteria may be scene and resolution dependent.

Therefore, future research should focus on a dynam-

ical adjustment of these weights, which may be per-

formed by previous scene analysis.

Figure 8 shows the edge features in the unman-

aged approach, as well as using our feature manage-

ment on four scenes. In ﬁgure 9 the translation and

rotation error are listed for one scene with and without

feature management. Comparing both approaches,

the error is smoothed by evaluating the feature quality

and leads to a more accurate pose, which shows in less

error spread concerning rotation as well as translation.

In the unmanaged approach a higher rate of jittering

occurrs and in some passes even results in corrupt or

lost camera poses due to unﬁltered false correspon-

dences. Concerning computation time, in the average

over all test scenes, the feature management outper-

forms the unmanaged approach by a factor of two.

This is due to early ﬁltering of the features, leaving

the visibility test, matcher and pose estimator with a

minimal set of features. In the best case feature eval-

uation and management could reduce the data load to

10% of 60 feature edges originally contained in the

model without losing tracking accuracy. On an HD

resolution video sequence using unmanaged tracking,

the framerate dropped below interactive rates. The

feature management approach could successfully per-

Figure 8: Edges used for tracking (red) in images from the

tracking scenes. Left: Unmanaged. Right: Feature manage-

ment.

form real-time tracking in about 10 ms per frame by

selecting the 6 best feature edges. Additionally, the

approach showed a remarkable improvement in pre-

cision on high resolution video.

4 CONCLUSIONS

The results show it is worth thinking about fea-

ture evaluation and management to improve tracking

speed and the accuracy of the estimated pose, which

is preferable especially on mobile devices. In contrast

to an unmanaged approach, evaluating the quality of

features allows for using models of arbitrary level of

detail, which may not have been created for tracking

purpose especially. Such models are often present in

industrial scenarios where applications of augmented

reality may be used for teaching, planning, assembly

and maintenance purposes. Augmented reality is also

getting popular with touristical applications. In the

near future even 3D models of whole cities and par-

ticular touristical attractive buildings will be available

(e.g. Google Earth) or can be made available easily

with small effort, which expands the possibilites for

ubiquitous urban tracking.

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568

-1.5

-1

-0.5

0.5

1.5

200 250 300 350 400

Rotation error

Frames

Scene2: All-In Rotation Error

Rotation X

Rotation Y

Rotation Z

-0.04

-0.03

-0.02

-0.01

0.01

0.02

0.03

0.04

0.05

200 250 300 350 400

Translation error

Frames

Scene2: All-In Translation Error

Translation X

Translation Y

Translation Z

-1.5

-1

-0.5

0.5

1.5

200 250 300 350 400

Rotation error

Frames

Scene2: Feature Management Rotation Error

Rotation X

Rotation Y

Rotation Z

-0.04

-0.03

-0.02

-0.01

0.01

0.02

0.03

0.04

0.05

200 250 300 350 400

Translation error

Frames

Scene2: Feature Management Translation Error

Translation X

Translation Y

Translation Z

Figure 9: Pose error in one tracking scene without (top) and with feature management (bottom) for rotation (left) and transla-

tion (right). The error is shown separately for all coordinate axes.

ACKNOWLEDGEMENTS

This work was supported by grant no. MU 2783/3-1

of the German Research Foundation (DFG).

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