Sparse Scene Flow Segmentation forMoving Object Detection in Urban Environments
Philip Lenz, Julius Ziegler, Andreas Geiger and Martin Roser
Institute of Measurement and Control Systems
Karlsruhe Institute of Technology
D-76131 Karlsruhe, Germany{lenz | ziegler | geiger | roser}@kit.edu
Abstract— Modern driver assistance systems such as collision
avoidance or intersection assistance need reliable informationon the current environment. Extracting such information fromcamera-based systems is a complex and challenging task forinner city traffic scenarios. This paper presents an approachfor object detection utilizing sparse scene flow. For consecutive
stereo images taken from a moving vehicle, corresponding
interest points are extracted. Thus, for every interest point,
disparity and optical flow values are known and consequently,
scene flow can be calculated. Adjacent interest points describing
a similar scene flow are considered to belong to one rigid
object. The proposed method does not rely on object classes
and allows for a robust detection of dynamic objects in traffic
scenes. Leading vehicles are continuously detected for several
frames. Oncoming objects are detected within five frames after
their appearance.

I. I NTRODUCTION
Inner city traffic is a very complex and demanding scenario for modern driver assistance systems (DAS). Different
applications such as collision avoidance, lane keeping or intersection assistance need reliable information on the current
traffic situation.
Perception and understanding of highly dynamic traffic
scenes is crucial for such systems. Urban traffic is more
complex than highway traffic, thus the task for DAS ismore challenging and currently an unsolved problem: Traffic
scenes are crowded with many different types of traffic
participants such as cars, pedestrians, cyclists or trams whichmust be distinguished while the surrounding scenery may
differ arbitrarily. Two typical scenarios for rural roads andinner city traffic are given in Fig. 1. In these situations, theDAS itself must work reliably with low error-rates and reactcorrectly to abruptly changing scenarios considering objects
in a wide range (from close to the hood to more than 50 m
ahead).
The goal of our approach is a class-independent detectionof moving objects for inner city traffic scenarios without
applying a previous training step. The proposed method provides three-dimensional (3D) position information of moving
objects in a world coordinate system. We believe that thisinformation constitutes valuable input to downstream processing steps, which enable functions like tracking, trajectory
estimation, and, ultimately, a high-level description of thecurrent environment.
In this work we apply a stereo camera for object detection.
By exploiting four images at once, stereo and optical flow(a) Rural Road Traffic(b) Inner City Traffic
Fig. 1. Typical traffic situations for rural and inner city scenarios.
The road geometry of highways and rural roads (a) is rather clear compared
to arbitrary inner city streets (b) where distinctive features may be missingor misleading. Due to the high degree of diversity of traffic participantsand views, class dependent object detectors will be error prone for DAS
applications.

information are obtained. Since for every detected point the
3D position and the optical flow between two consecutive
frames is known, a sparse scene flow description [27] of thescene is achieved, describing 3D motion of world points.
Since interest points can be tracked over several frames, thescene flow is computed via finite differences for a track
up to five 3D positions. Utilizing this information, points
describing a similar scene flow are grouped together belonging to corresponding rigid objects in the scene. Adjacentpoints are connected using a Delaunay triangulation and theresulting edges are removed if the scene flow difference
exceeds a certain threshold. As the error of the 3D position
grows quadratically with the distance, error propagation of
the scene flow computation is taken into account to remove
edges by thresholding the calculated Mahalanobis distance.
Pedestrians and cars are detected within a reasonable range
for inner city intersection scenarios. Detected objects infront of the observer vehicle are tracked for up to 15 s andinfrequent traffic participants such as wheelchair users aredetected as well.

Fig. 2. System Overview. (a) Interest points are detected in two consecutive stereo image pairs. (b) Stereo disparity and optical flow information lead toa 3D reconstruction of the detected points which are here projected on the detected ground plane. (c) Interest points are connected in a graph-like structure
and the resulting edges are removed (marked in red) if the scene flow difference exceeds a certain threshold. (d) Remaining connected components describe
moving objects in the scene.

The paper is structured as follows: In the next section,
related work is presented and distinguished from our work.
An overview of the method is given in Section III before theobject detection is discussed in more detail in Section IV.
Some results are shown in Section V before the paper isconcluded by a summary and an outlook.
II. P REVIOUS W ORK
Robust scene perception in urban environments is a current
field of research [1], [8], [9], [13], [18], [21], [22], [28]and is investigated by applying different sensors. LIDAR
systems offer a high geometric accuracy for 3D world points
that can be used to detect multiple objects under groundplane assumption [28] or by using a model-based object
detector [17]. Recent work shows, that motion estimationfrom range images gives promising results as well [21].
While LIDAR can directly provide important 3D information, the required data lacks of rich appearance. This iswhere camera based systems bear high potential. Several
approaches for object recognition in the image domain exist
to detect single [6], [20] or multiple [11], [26] object classes.
However, this task is even harder, if the observing camera
is moving and egomotion is affecting the image acquisition
due to blur or changing lighting condition [5], [19]. Thesemethods generally use an uninformed search over the whole
image and discard further information on the scene geometry. Additionally, environment perception for DAS does not
only require detected objects in the image domain but also
demands a 3D description of the observed traffic scenario
in order to perform e.g. collision avoidance. The 3D scene
geometry can be estimated using only a single frame as well,
but several restrictions must apply and the scene must be richof known objects [16].
A stereo camera system allows for geometric reconstruction of the scene in a 3D world coordinate system [15].
Nedevschi [23], [24] introduced a system for inner city object
detection and classification utilizing a stereo camera system.
Dense stereo information is used for reconstruction and 3D
data used for model selection and scale estimation. Object
classification relies on pattern matching exploiting object
scale, orientation and location in the scene obtained by aprevious model selection step. Franke [13] and Wedel [29]utilize stereo data to estimate 3D position and 3D motionof interest points for a robust detection of moving objects.
Pfeiffer [25] introduced the tracking of stixels obtained fromdense stereo images as a representation for suburban traffic
scenarios.
Mueller [22] evaluated the optical flow difference ofadjacent interest points to group them describing independent
objects in the scene. This approach utilizes interest points
detected in a monocular image sequence which are connected
in a graph-like structure and is most similar to the proposed
one. In contrast, we are using scene flow information for
detected interest points since the approach in [22] uses
constraints describing motion of rigid objects in the image
plane that are not strong enough for inner city scenarios.
Ess [9] uses the HOG framework [6] for object detection
and consequently multiple object tracking for inner city
traffic scenes. For this application, it turned out to be superior
compared to [11], [20] since the shape variations of traffic
participants are relatively small [9].
III. S YSTEM OVERVIEW
Figure 2 outlines our system: Interest points are detected
in two consecutive and rectified stereo images and checked
for mutual consistency. The resulting disparity values d leadto a 3D description for every detected interest point. Due tothe unknown mounting position of the stereo camera rig andthe pitching of the vehicle, the ground plane is detected toobtain a meaningful description of the surrounding. Everyinterest point is tracked over time and its scene flow is
calculated using finite difference approximation to yield
derivatives. The associated covariance is obtained by linear
error propagation.
A graph-like structure connecting all detected interest
points in the image plane is generated using Delaunay
triangulation [2]. The resulting edges are removed according
to scene flow differences exceeding a certain threshold withrespect to the uncertainty of the computed 3D position of
every interest point. The remaining connected componentsof the graph describe moving objects in the scene. Detected
objects are tracked over time using a global nearest neighbor(GNN) approach [3].

IV. O BJECT D ETECTION

∆ti

a0

a1

a2

a3

a4

a5

The configuration of our stereo camera rig is depicted inFig. 2. Two consecutive stereo images are considered. The
world reference frame is denoted with OW and coincides
with the left camera frame. Consequently the extrinsic calibration of the setup {RC , tC } is known and assumed to beconstant. Assuming a proper, calibration we will use rectified
input images for our algorithm, so that we only have todeal with a horizontal search line for correspondences inthe stereo image pairs.

1
2
3
4
5

1
3/2
11/6
25/12
137/60

−1
−2
−3
−4
−5

1/2
3/2
3
5

−1/3
−4/3
−10/3

1/4
5/4

−1/5

TABLE IC OEFFICIENTS FOR FINITE BACKWARD DIFFERENCES .

A. Scene Flow Computation
Interest points x = [u, v]⊤ are detected in two consecutive
stereo image pairs at time k, k−1 using the algorithm of [14].
Only reliable feature correspondences which match in a loop
are kept (xL,k−1 ↔ xR,k−1 ↔ xR,k ↔ xL,k ↔ xL,k−1 ).
Since the signal-to-noise ratio is rather low considering only
the previous frame, detected interest points are associatedand stored as tracklets for up to 5 time steps.
Since rectified images are used and the disparities are
estimated at sub-pixel accuracy, the extracted interest points
xk−1 , xk are mapped to 3D points in the world coordinate
system Xk−1 , Xk with X = [X, Y, Z]⊤ ∈ R3 . The reconstruction is given by(uL − cu,L ) · b(1)d(vL − cv,L ) · b
Y =
(2)
d
b·f
(3)
Z=
d
where b denotes the baseline of the stereo system, cu andcv the principal point of the camera and f the focal length
for the rectified images. The center of the left-handed world
coordinate system is OW with the X axis pointing to theright as depicted in Fig. 2.
The velocity V of every world point is assumed to beconstant within the tracked time t of 5 frames (t = 0.5 s).
Thus, the velocity is computed as the first order derivativeof the world points Xk−∆ti :(a) Model of the 3D reconstruction error.

X=∆Xk−∆ti(4)∆t
The velocity is measured at discrete time steps∆ti , i = 0 . . . 5 at a constant sampling rate of 1/∆t = 10 Hz.
This assumption of an equispaced grid leads to thecalculation of the coefficients for the first order derivativef ′ depending on the tracked time steps according to [12]:V=

f ′ (x) ≈ a0 f (x) + a1 f (x − 1) . . . + a5 f (x − 5)(5)

Since the proposed algorithm is designed for automotive
applications, the delay between occurrence and detection ofan object should be minimal. Therefore, backward differences are used to determine the scene flow. The coefficientsare listed in Table I. An example of the resulting scene flow
is shown in the system overview in Fig. 2.

(b) Quadratically growing reconstruction error.
Fig. 3. Resulting noise of 3D reconstruction. One pixel noise of thedisparity uL −uR leads to the 3D position Xk and the associated covarianceellipsoid Σk (a). Error propagation leads to a quadratically growing errorof the 3D position with the largest uncertainty perpendicular to the line ofsight (b).

B. Scene Flow Clustering
The computed scene flow is now clustered into groups
describing a similar motion. For this purpose, a graph-likestructure is built up where interest points are considered as
nodes and the adjacent nodes are connected by edges. To
obtain this structure, a Delaunay triangulation [2] as shown in
Fig. 4(a) is applied to determine neighboring interest points
for further processing steps.
Considering the absolute scene flow difference Vi − Vjof adjacent nodes i, j and removing the correspondingedges does not lead to a satisfying solution as shown in
Fig. 4(b). The reconstructed 3D position is error-prone due
to measurement noise. The resulting error of the 3D position
is computed by linear error propagation. The error grows
quadratically with the distance as depicted in Fig. 3, thus afixed threshold on the scene flow difference is not suitable.
Therefore, the Jacobian J of the scene flow for a 3D world
point X is calculated according to equation 6 with respect
to all image coordinates uL , uR , v for all possible time stepsk − ∆ti .



J=

∂VX
∂uL,k−∆ti

dV 
 ∂VY
=  ∂uL,k−∆t
i
dx


∂VX
∂uR,k−∆ti∂VX ∂vk−∆ti∂VY∂uR,k−∆ti∂VY ∂vk−∆ti  , i = 0 . . . 5

∂VZ

∂VZ

∂uL,k−∆ti∂uR,k−∆ti∂VZ∂vk−∆ti(a) Interest point triangulation(6)

The covariance Σ of the scene flow is given byΣ = JSJ⊤(b) Fixed threshold(c) Thresholding of Mahalanobis distanceFig. 4.
Graph building and clustering. Detected interest points areconnected using a Delaunay Triangulation (a). A fixed threshold to remove
the edges considering the scene flow difference does not lead to a satisfying
solution (b). Considering error propagation and applying a threshold on the
resulting Mahalanobis distance groups similar objects in the scene (c).

where S is the diagonal measurement noise matrix assuming
a measurement noise of 0.5 pixel. Assuming the noise to be
Gaussian, for two adjacent nodes i and j, the Mahalanobis
distance ∆ is given byq(8)∆(Vi , Vj ) = (Vi − Vj )⊤ Σ−1i,j (Vi − Vj )according to [7]. Each edge is weighted 0 (or removed) if∆ exceeds a certain threshold. Consequently, the remaining
subgraphs contain nodes with small scene flow differences
and similar motion describing a rigid object as shown in
Fig. 4(c). The remaining connected components in Fig. 5(a)of the graph are detected by depth-first search [4].
Since the egomotion of the observing vehicle is notknown, the algorithm responds to static parts of the scene
as well. However, static parts describe large 3D volumes or
are typically part of the ground plane. Therefore, objects
exceeding reasonable dimensions are neglected as well as
objects that are part of or not standing on the ground plane.
Both latter criteria are achieved by estimating the parameters
π1 . . . π4 of the ground plane
E : π1 X + π2 Y + π3 Z + π4 = 0(a) Remaining connected components

(7)

(9)

using a random sample consensus (RANSAC) algorithm.
C. Object Association(b) Geometrically checked objects(c) Tracked objects
Fig. 5. Object detection. All remaining connected components of thecomputed graph are found by depth-first search (a). Geometric features aretaken into account to remove static parts of the scene. Objects are coveredby a bounding box, center and the velocity of the object are marked in theirrespective color (b). Object tracking and expecting a detection in at least
two time steps leads to the final detection (c).

The last step is to form tracks produced by the same object
within the considered sequence. The Observation-to-Track
Association is handled by a GNN approach [3]. The position(i)
Xk−1 of a detected object io is predicted for the current time(i)step Xp,k assuming a constant motion within ∆t. For every(i)predicted object, a constant track gate is placed around Xp,kconsidering acceptable measurement and prediction error as
shown in Fig. 6. If two observations are associated with one
predicted track, the unassigned observation will initiate a
new track. Objects are assumed to appear at least in two
consecutive time steps for confirmation and only one miss is
accepted before deletion.
The object association results in the detected objects in
Fig. 5(c). The blue erroneous object in Fig. 5(b) is notdetected since it only appears in this very time step. However,
objects are detected with one frame delay such as the policecar, which will initiate a track from the next frame on.

Fig. 6. Global nearest neighbor algorithm. Track gates are set around(i)the predicted positions Xp,k . For unambiguous gates one observation is
assigned to exactly one prediction (X(1) ). For a conflicting prediction such(2)
(3)
as Xp,k , the unassigned observation Xk initiates a new track.

V. E XPERIMENTS
For all experiments, we used grayscale images with aresolution of 1392 × 512 pixels. The image sequences
cover rural, suburban and inner city traffic scenarios. For
all images, at least 2000 interest points were detected. Ouralgorithm is implemented in Matlab processing at least one
frame per second on one core of an Intel Core2Duo with
2.4 GHz and 4 GB RAM.
To evaluate the capability of the proposed algorithm todetect objects continuously, the rural traffic scenario inFig. 7 is considered. Two vehicles in front are visible
within 115 frames in a distance of approximately 10 m and35 m respectively. Using the GNN approach described inchapter IV-C, both cars are detected continuously within
the whole sequence. Approaching cars in this sequence aredetected within at most three time steps after they are fully
visible and at a distance up to 25 m. For a greater rangeof up to 60 m, objects are detected within five time steps.
Since the maximum speed for this scenario is usually limited
to 60 km/h, we consider this time period as sufficient. After
detection of an object at a distance of 50 m, there are 2 sremaining for an appropriate reaction. Since our approachis class-independent, uncommon but moving objects aredetected as well, e.g. the wheelchair user on the left sidewalk in the second frame. We tested the appearance-based
detector of [11] using this image. Since this class was nottrained, the detection of a person or a bike in this image
fails, although for the class car there were detected objects
at close and far range.
Figure 8 shows that the detection at close and far range
up to 60 m works well in a rural environment. For innercity scenarios as depicted in Fig. 9, cars and pedestrians aredetected. The algorithm fails here for objects in a far range
for more than 50 m since stereo reconstruction exhibits aheavy noise level in depth direction and the background ishighly textured and therefore only few interest points aredetected on objects of interest. The tracks of turning cars
are very short due the assumption of continuous motion.

Fig. 7. Results on a rural road. The cars in front of the observer vehicle
are tracked continuously within 15 s. The class-independent method detects
the wheelchair user in the upper left area of the second frame. Static objects
such as the advertisement or trees are detected as objects since egomotion
is not compensated.

Fig. 8. Results on a rural intersection. Close range objects as the cyclist
or the crossing traffic in the first frame are detected as well as objects inthe far range in the second frame.

Fig. 9. Results on inner city intersections. (a) Slowly moving and small objects such as the pedestrian in the first two frames are detected in a range up
to 30 m. However, the track of such a small object is interrupted and it is not continuously tracked. Similarly moving groups of pedestrians are detected
as one object since the scene flow difference is not unique and the number of detected interest points is to low. (b) Moving objects in this sequence aredetected, but especially for the far range static objects are detected as well. (c) Turning cars and partly occluded objects, which where fully visible andobserved in a previous frame, are detected. Since continuous velocity is assumed, the Observation-to-Track association fails for sharply turning objects.

VI. C ONCLUSION AND F UTURE W ORK
We presented a novel approach for class-independent
object detection for inner-city traffic scenarios. The proposed algorithm uses computationally efficient sparse interest
points in consecutive stereo images to compute the scene
flow. Clustered points describing a similar scene flow resultin a robust detection of independently moving rigid objects
in the scene. Our results compared to [22] indicate, that thisapproach outperforms a similar method utilizing only opticalflow for object detection. Due to the class-independent
approach, objects are detected where an appearance-based
object detectors fails which works well for known object
classes. For typical inner-city traffic scenarios unknown
objects or objects in an uncommon view may occur and in
this case these approaches may be not sufficient for DAS
applications.
Our next steps will include a more sophisticated multiple
target tracking to improve the distinction between traffic
participants and erroneously detected objects, handle partly

occluded objects as well as erroneous track gates. Additionally, tracking of objects coming to a stop will be improved.
Further, we want to include egomotion compensation anda motion model for detected interest points and objects.
Thereby rotation of detected objects will be explicitly considered. Furthermore, a global solution to remove edges in
the graph structure will be investigated. Although we want tofollow our class-independent approach for object detectionin the first step, semantic information should be provided
after the initial object detection for higher-level reasoning,e.g. for navigation decisions of a DAS. Since most of theexisting large object databases, e.g. [10], are not providing
the required data for our approach, a ground truth data basefor our application will be built up for evaluation.
VII. ACKNOWLEDGMENT
The authors would like to thank the “Karlsruhe Schoolof Optics and Photonics” and the “Deutsche Forschungsgemeinschaft” for supporting this work.

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