ObjectFlow: A Descriptor for Classifying Traffic Motion
Andreas Geiger and Bernd Kitt
Institute of Measurement and Control Systems
Karlsruhe Institute of Technology{geiger,bernd.kitt}@kit.edu

Abstract— We present and evaluate a novel scene descriptor
for classifying urban traffic by object motion. Atomic 3D flow
vectors are extracted and compensated for the vehicle’s egomotion, using stereo video sequences. Votes cast by each flow vector
are accumulated in a bird’s eye view histogram grid. Since weare directly using low-level object flow, no prior object detection
or tracking is needed. We demonstrate the effectiveness of the
proposed descriptor by comparing it to two simpler baselines
on the task of classifying more than 100 challenging video
sequences into intersection and non-intersection scenarios. Our
experiments reveal good classification performance in busy
traffic situations, making our method a valuable complement
to traditional approaches based on lane markings.

(a) Intersection scenario

I. I NTRODUCTION
Vision-based intersection detection and recognition [10],[26], [23], [20], [9] is an important task for advanced driverassistance systems and autonomous driving. However, it isalso highly difficult for several reasons.
First, nature’s diversity in appearance is hard to captureusing a computer algorithm: While large and small intersections exist, they may be marked or unmarked. Traffic lights
and signs may or may not be present. Also, the surrounding
scenery can be either natural (e.g., trees and bushes) or manmade (e.g., buildings and brigdes).
Second, the tilted installation angle of wide angle carmounted cameras offers rich details in the cars vincinity, but
only little details at the region of interest (at distances ≥ 15
meters).
Third, other vehicles, which show up in a variety ofdifferent colors and shapes, often occlude major information
sources of the scene such as lane markings. Hence, approaches based solely on lane markings are not applicable tobusy traffic situations, which make up for most of the innercity scenarios. A typical ’intersection’ and ’non-intersection’image frame is depicted in figure 1, demonstrating the
aforementioned difficulties.
In recent years, much research has been conducted invision based road geometry estimation and tracking [3], [29],[18], [5], [28], [6], [2], [8], [27], [22], [24]. However, mostof the approaches use features based on lane markings andassume an unobstructed view onto the road, which is often
not the case in innercity scenarios. Furthermore a simple
road model based on splines or clothoids is used, which does
not naturally allow for representing more complex multi-lane
scenarios like intersections.
In contrast to our approach, [16], [17], [13] exploit digitalroad maps to generate road models which are mapped(b) Non-intersection scenario

Fig. 1. A typical intersection and a typical non-intersection scenario.
The task of detecting intersections is aggravated by the diversity in scene
appearance, the tilted installation angle of the camera and large occlusions,which mainly caused by other traffic participants. Approaches solely basedon lane markings will fail in such situations.

into the scene and matched against road features in theimages, also assuming an unobstructed view onto the road.
In [25] feature maps based on aerial images are generated
for the same purpose. Deductive inference is used in [19],in combination with description logic to narrow down thespace of plausible intersection hypotheses.
In [30] a Kalman-filter based tracking system is presented,
which tracks vehicles and pedestrians at intersections. However, in their scenario a static camera on top of a building
was used, making position estimation less noisy than in
our setting, where depth estimates have to be deduced fromdisparities and the observer is moving.
In [23] road border discontinuities and lane markings are
estimated to detect intersections from imagery. In [9] simple
intersections without traffic are recognized by detecting roadclothoids and their intersecting lines. An active camera setup
is proposed in [20] for the same task. First classification
approaches were conducted in [26], where color appearance
has been used to segment the shape of the road in a rectified
view into several intersection types using a binary support

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Fig. 2. System overview. By matching feature points and computing the vehicles egomotion using visual odometry we extract motion compensated 3D
flow vectors from stereo video sequences (left). Flow vectors from the last 100 frames are accumulated into one common coordinate system and projected
onto the ground plane (middle). A histogram grid is extracted by feature-based voting and serves as input to classification (right). Note that the rightturning vehicle is not present at the first frame of the sequence which is shown on the left for illustration purposes.

vector machine. [10] employs super-pixel segmentation to
construct feature sets, which are fed into a boosting-basedclassifier to distinguish between different road types anddetect cars, pedestrians and crossings.
In this paper, we present a descriptor for traffic classification which is easy to compute and complementary to
existing lane-marking based approaches. Instead of focusing
on lane-markings and curbstones, we robustly compute theflow of scene objects like bicycles, pedestrians and cars. This’object flow’ is accumulated in a local 2D map from which
histogram grid features are computed, based on a voting
scheme. Unlike the map-mosaicing approach proposed in[12], this allows for a memory-efficient implementation,
since only a sparse set of vectors has to be stored temporarily. Using a large margin classifier, we find optimal
boundaries for classifying these features into the two classes’intersection’ and ’non-intersection’ without prior streetmap
knowledge.
II. S YSTEM OVERVIEW
Figure 2 gives an overview over our system: First, sparseimage features are extracted and matched sub-pixel accurately against spatially and temporally neighboring frames. A
robust egomotion approach (section III) yields the extrinsic
motion parameters between consecutive frames, which areused for extracting motion compensated 3D object flow
vectors from the feature matches. We accumulate these
vectors into a local map, containing approximately the last
100 frames. Voting, based on the object flow position andorientation finally gives a global histogram grid descriptor
which is fed into a Support Vector Machine for classification(section IV). Our approach is evaluated on more than 100
video sequences (section V). We conclude this paper withan outlook on future work.
III. V ISUAL O DOMETRY
Due to our assumption of a moving observer, static partsof the scene have to be compensated for the egovehicle’s

PR, k

PL, k

RC, tC
RR, tR
RL, tLZW

PR, k-1

PL, k-1

XW

OW

YW

R C , tC

Fig. 3. Camera configuration. This figure illustrates the geometrical
relations of our stereo-camera-rig at two consecutive time steps. The originof the world coordinate frame is denoted by OW .

movement. The computation of egomotion further allows
for registering object flow vectors into a local map. In our
experiments we use a time span of 10 seconds, corresponding
to ≈ 100 image frames.
Figure 3 shows the configuration of our moving stereocamera-rig at two consecutive time-steps. Without loss ofgenerality, the world reference frame coincides with thecamera coordinate frame of the previous right camera. Using
this assumption, the pose of the left camera with respect tothe right camera is given by the extrinsic calibration of thestereo camera rig {RC , tC } which is assumed to be fixedand known. Note that self-calibration methods like [7] could
be used to relax this assumption. The poses of the current
cameras with respect to the previous right camera ({RR , tR }and {RL , tL }) are defined by the unknown egomotion andthe extrinsic calibration parameters.
To parameterize the camera motion, i.e. the orientationof the right camera coordinate frame with respect to theworld reference frame, we use a translation vector t =T(tX , tY , tZ ) and a rotation matrix R (Θ, Φ, Ψ) which is aconcatenation of rotations around the coordinate axis of theworld reference frame. With the knowledge of the egomotion
T(VX , VY , VZ , ωX , ωY , ωZ ) and the time difference ∆T thistransformation is readily given.
A. Trifocal Constraints for Egomotion Estimation
As can be seen from figure 3, the projection matrices of allcameras {PR,k−1 , PL,k−1 , PR,k , PL,k } can be computed
based on the calibration and the egomotion of the stereo rig.
The geometric relationship between three of these cameras,
i.e. both cameras at frame k − 1 and one of the cameras at
frame k respectively, can be expressed by the trifocal tensor
T [15]. This tensor encapsulates the projective relations
between three views of a scene. Its entries are determined
by


∼ ai
i+1
Tiqr = (−1)· det  bq  ,(1)
crwhere ∼ ai represents matrix PA without row i and bq
and cr are the q-th row of PB and the r-th row of PC
respectively. Given the trifocal tensor and matching image
points xA ↔ xB in two images, the corresponding image
point xC in the third image can be determined using thepoint-line-point transfer [15]. This transfer can be expressedby a non-linear mapping via xj,k = hj (Tj , xR,k−1 , xL,k−1 )with j ∈ {R, L} defining the current camera.
Thus, the relationship between the trifocal tensor and point
correspondences in three images can be used to determine
the egomotion of the stereo rig.
In a first step, we use a Laplacian-of-Gaussian (LoG)
approximating interest point detector (e.g., CenSurE [1]) toquickly detect blob-like features in the current and previous
stereo pair {IR,k−1 , IL,k−1 , IR,k , IL,k }.
Second, we extract MuSURF (modified upright SURF) descriptors [1] and match them using the l1 -norm for robustness
and efficiency. Only reliable correspondences which matchin a loop (xL,k−1 ↔ xR,k−1 ↔ xR,k ↔ xL,k ↔ xL,k−1 )are kept. Note that we are using rotation-variant features
since exaggerated roll motion between consecutive frames
is unlikely.
Third, a bucketing [31] technique is used to reduce thenumber of features. This results in several advantages: On
one hand, the number of correspondences is reduced, hence
also the computational complexity of the filtering algorithm.
On the other hand, all image features are approximately
uniformly distributed over the image plane. This is important,since far features are crucial for reliably estimating theangular velocity and near features guarantee an accurate
estimation of the longitudinal vehicle velocity. Furthermore,
bucketing guarantees that no biasing towards features on
independently moving objects is present, which results ina more stable motion estimation especially in highly dynamic environments. The remaining feature points locatedon independently moving objects as well as false featurecorrespondences are rejected in an iterative manner using

an iterated Extended Kalman-Filter (IEKF) described in thefollowing section.
B. ISPKF based Motion Estimation
To reduce the impact of noisy measurements and to include knowledge about the dynamic behaviour of the vehicle
we use a Kalman Filter based state estimation. Because of the
non-linearities in the mappings induced by the trifocal tensor
we use an Iterated Sigma-Point Kalman-Filter (ISPKF),
which linearizes the observation model. For discrete-timesystems [14], the state and observation model are given byyk+1= f (yk ) + wk

(2)

zk+1

= h (yk+1 ) + vk+1 .

(3)Here, h (.) is the non-linear measurement equation as
described in section III-A, w and v are the system
noise and measurement noise, respectively. The state y =T(VX , VY , VZ , ωX , ωY , ωZ ) is defined by the egomotion of
the vehicle and the measurements z are the locations of thematched feature points in both current images. Since we aremaking a constant velocity assumption we have f (y) = y.
Compared to non-iterative filtering, the application of an
ISPKF yields two central benefits: First, the linearization
error can be reduced which results in more accurate motion
estimates. Second, we eliminate feature correspondences notconsistent with the current estimate by applying RANSAC
on a random subset of the features. In a final step, all inliers
are used. More details can be found in [21].
IV. O BJECT F LOWThis section describes how we compute the object flow by
extracting 3D flow vectors. Note that our method is related
to the approach proposed in [11], however we do not employ
Kalman filters to track single features over time, but rather
project atomic flow vectors onto the ground plane (i.e., bird’s
eye perspective). The flow is further processed in a votingbased procedure to give the final histogram descriptor which
is used for classification.
A. Local Flow Accumulation and Filtering
Since we are using a stereo system with disparities estimated at sub-pixel accuracy, we can use triangulation to map the already extracted image features
xL,k−1 , xR,k−1 , xR,k , xL,k , xL,k−1 (section III) to 3D
points Xk−1 , Xk with X ∈ R3 . Without loss of generality
we define those points in the coordinate system of the rightcamera. To compensate for the observer’s motion and to
separate the static scene part from dynamic objects, 3D
points from frame k − 1 are mapped into the coordinate
system of frame k according to Xk−1+(4)
Xk−1 = Rk−1→k tk−1→k1
where {Rk−1→k , tk−1→k } denotes the vehicle’s egomotion
between frames k − 1 and k. The 3D flow vector at frame k

is finally given byXk ∈ R6
ξk3D =  Xk − X+
k−1
∆Tk−1→k(5)with time difference ∆Tk−1→k . The first 3 elements of ξk3D
represent the flow vector’s location (in meters) in frame k’s
coordinate system and the last 3 elements are its velocity (inmeters per second). To account for outliers and static objects
in the scene we apply three additional steps:
First, we reject all flow vectors for which the compensatedoptical flow (reprojection into the image plane) does notexceed 8 pixels. We further keep only flow vectors which
are positioned at plausible distances (5-50 meters). Last, wemlimit the flow vector speed to values between 0.3 m
s and 30 s .
This filtering step is illustrated in figure 2 (left).
Since disparity depth estimates are usually highly sensitive
to calibration errors and noisy at large distances, a 3D medianfilter is adopted at each frame with a kernel size of 3
meters. Finally, temporal integration is achieved by building
a local 2D map. To this end, all flow vectors ξ 3D from thelast 10 seconds are projected into one common coordinate
system, namely the current one at frame k, making use of theegomotion estimates from section III. Knowing the camera’s
pitch and roll angle, we project the aggregated flow vectors
into 2D (bird’s eye perspective), giving N object flow vectors
N
ξi2D = (pix , piz , vxi , vzi )T i=1 at position pi = (pix , piz )T andwith velocity vi = (vxi , vzi )T . Figure 2 (middle) illustrates
the object flow vectors extracted from a typical intersection
scenario.
B. Voting-based Flow Description
Since our final goal is classification, a fixed-sized descriptor must be extracted from the variable-sized object
flow vectors described in the previous section. We tackle
this problem by employing a voting scheme, in which eachflow vector casts votes for its own direction on a fixed 2D
grid of size Mx × Mz . Flow vectors are sparse, but canbe extrapolated to a certain extend. Thus we decided foranisotropic voting weights according to the motion direction(see figure
we denote the grid loca 4). In the following,
Mx ×Mz. In order to accumulate
tions by qj = (qxj , qzj )T j=1
all votes, each grid point j is represented by a histogramhj ∈ RMθ , quantizing the object flow into Mθ orientations.
Thus, the proposed descriptor becomes a vector of size
Mx × M z × M θ .
Every object flow instance ξi2D = (pix , piz , vxi , vzi )T at
position pi casts one vote for its orientation vi to eachhistogram hj on the grid, located at qj = (qxj , qzj )T .
The weight of each vote w(q, ξ 2D ) depends on the grid
point q and the flow ξ 2D itself via
w(q, ξ 2D ) = exp −(q − p)T W(q − p) ∈ [0..1] . (6)Here W ∈ R

2×2

is defined by its eigenvalue decomposition
W = RΛRT

point indescriptor grid

basis

(7)

voting weight
contour levels
measuredobject ﬂow

time ttime t-1

Fig. 4. Illustration of the voting procedure. This figure depicts the votes
casted by an object flow vector at position p = (0, 15)T on a grid of size
Mx = Mz = 16. The size of the circles and the constant-level countours
illustrate the weight of the vote each grid point receives for the orientationof the measured object flow.

with
R=



r1
r2

r2
−r1



Λ=



1/λ21
0


0
.
1/λ22

(8)

Λ and W are mathematically similar and R is a orthonormalchange-of-basis matrix, defined by the orientation of therespective object flow ξ 2D :vxvz
r1 = pr2 = p(9)222vx + vz
vx + vz2

Here, the parameters λ1 and λ2 control the longitudinal andlateral influence of each flow vector.
The weight calculation step is illustrated in figure 4 forone single flow vector and a grid of size 16 × 16. After
accumulating the votes from all object flow vectors, each of
the Mx × Mz histograms hj is individually normalized (i.e.,
divided) by max(10, max(hj )). Concatenating all histograms
finally yields the object flow descriptord = (h1 , ..., hMx ×Mz ) ,(10)a vector of size Mx ×Mz ×Mθ . Figure 2 (right) illustrates anobject flow descriptor with Mx = Mz = 16, where the arrow
directions represent Mθ = 8 canonical histogram orientations
and the arrow lengths depict the relative number of votes foreach orientation.
V. E XPERIMENTS
A qualitative assessment of the proposed method is given
in figures 5,7. Figure 7 depicts in each row two frames ofthe sequence, the extracted object flow and the computed

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Fig. 5. Object flow of a lane change maneuver. This figure depicts theobject flow field of a preceding car. While the first car changes lanes fromthe left to the right, the observer changes lanes in the opposite direction.

descriptor using different scenarios. Note that not only abinary classification, but also regressing different intersection
types should be possible and promises worthwhile avenues
for future research. Figure 5 shows the egovehicle, its motion
computed using the visual odometry approach described in
section III and the object flow of a preceding vehicle. Bothvehicles perform a lane change in the opposite direction ofeach other. While some outliers remain, most flow vectors
are correct.
To evaluate the proposed descriptor quantitatively, werecorded 113 grayscale video sequences using our carmounted stereo rig at a framerate of 10 fps, with 1382 × 512
pixels and an opening angle of 90 ◦ . Each sequence contained
approximately 10 seconds, which was empircally found tobe well suited for our method. We manually classified thesequences into 65 positive (intersections) and 48 negative(non-intersections) examples.
For comparison, two baselines were created: The firstbaseline, called ImageFlow 2d accumulates sparse opticalflow vectors of the left camera in a single histogram witheight bins corresponding to eight canoncial directions in
the image. The second baseline, called ObjectFlow 2d isessentially the same, except that the optical flow vectors in
the image were compensated for the vehicles egomotion prior
to histogram voting, using the technique described in section
III. The idea behind both baseline algorithms is to capture themain optical flow direction in the image: A high horizontal

Method (w,s)
Img.Flow 2d
Obj.Flow 2d
Obj.Flow (2,2)
Obj.Flow (3,5)
Obj.Flow (5,10)
Obj.Flow (15,30)

TP
54
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32
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Fig. 6. Classification results. This figure illustrates our classification
results on 113 recorded sequences for both baselines (ImageFlow 2d andObjectFlow 2d) and our method (ObjectFlow) with 4 parameter sets, varying
the size of the exponential voting kernel w and the grid step size s.

optical flow ratio should be expected at intersections while
non-intersection scenarios are supposed to produce mainly
vertical flow vectors in the image. However, both baselines
make no use of depth information, except for compensating
the egomotion (ObjectFlow 2d).
To account for the high dimensionality of the ObjectFlow
descriptor proposed in section IV, we extended the training
set by shifting all examples by ±2 meters in the x/z plane.
Sure enough, we did not include the translated instances of
the test data into the training set of the respective run for
fairness reasons.
For classification we employed a soft-margin support vector machine (SVM), making use of the libsvm library [4]. A
linear kernel was chosen to avoid overfitting due to the highdimensional nature of the feature space (Mx × Mz × Mθ ).
Since the number of recorded scenarios (113) is relatively
small compared to the high-dimensional feature space, leaveone-out cross-validation was employed.
Figure 6 shows the result of our experiments in terms oftrue positives, false positives, true negatives, false negatives,precision and recall. Here a ’true positive’ denotes a correctly
detected intersection. We compare the two baselines to our
method using four sets of histogram grid parameters. The
parameter w adjusts the influence area of the exponential
voting kernel from equation 8 via
λ1 = 3w

λ2 = w

(11)

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Fig. 7. Results on 2 non-intersection and 3 intersection sequences. Each row depicts one scenario: The left column shows two image frames of theleft camera, the middle column displays the extracted object flow from a bird’s eye perspective (unit: meter) and the right column depicts the histogram
grid descriptor using a step size of s = 5 meters.

and s is the histogram grid step size, e.g. s = 2 in figure 4
and s = 5 in figure 7.
While the ImageFlow 2d and ObjectFlow 2d baselines
perform quite well already, adding depth information clearly
helps in discriminating intersection scenarios versus nonintersection scenarios. For all parameter sets of the ObjectFlow descriptor no false positive has been reported.
The false negative rate differs slightly among the parametersettings with the lowest rate for w = 5 and s = 10.
We believe that this ’averaging’ parameter set is mainly
preferred as a consequence of the relatively small amountof training examples. Still, all results clearly indicate thebenefits of stereo-based measurements with a precision of
1.0 and a recall ≥ 0.89. Our system currently runs on asingle CPU core at about 2 frames per second. Since the mostcomputationally expensive part is feature matching, real-time
can be achieved with a GPU or FPGA-based feature matcher.
VI. C ONCLUSION AND F UTURE W ORK
In this paper a complementary novel descriptor for visually classifying traffic motion into intersection and nonintersection scenarios from within a moving vehicle has
been proposed and evaluated against two baseline algorithms.
Our findings indicate that including depth information into
the classification process clearly outperforms classifiers withimage based motion features. Though our results are promising, we intend to include more image sequences to thetraining data base in the future. Further, we plan to investigate other features for matching as well as combining thecomplementary object flow features with road based ones
into local maps. Robustly fitting road topography models
to this data promises valuable directions for future research
towords advanced driver assistance systems and autonomous
driving in difficult urban scenarios.
VII. ACKNOWLEDGMENTS
We would like to thank the Karlsruhe School of Optics
and Photonics and the Deutsche Forschungsgemeinschaft for
supporting this work and the reviewers for their comments.
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