=Paper=
{{Paper
|id=Vol-1197/paper10
|storemode=property
|title=Moving Object Detection in Video Streams Received from a Moving Camera
|pdfUrl=https://ceur-ws.org/Vol-1197/paper10.pdf
|volume=Vol-1197
|dblpUrl=https://dblp.org/rec/conf/aist/StarkovL14
}}
==Moving Object Detection in Video Streams Received from a Moving Camera==
https://ceur-ws.org/Vol-1197/paper10.pdf
Moving Object Detection in Video Streams
Received from a Moving Camera
Sergey Starkov, Maksim Lukyanchenko
National Research Nuclear University MEPhI, Obninsk, Russia
starkov@iate.obninsk.ru, maksim.lukyanchenko@gmail.com
Abstract. Detection of moving objects in a video stream received from
a moving camera is difficult computer vision task, because the motion of
the camera blends with the motion of the objects in the scene. In order
to tackle this problem, we propose a method based on optical flow cal-
culation and Delaunay triangulation. Given a sequence of frames, firstly,
we extract the corner feature points using ORB algorithm and compute
optical flow vectors at the extracted feature points. Secondly, we sepa-
rate the optical flow vectors using K-Means clustering method. Third,
we classify each cluster into camera and object motion using its mean
scatter value. Finally, we represent the moving object using Delaunay
triangulation.
Keywords: moving objects, moving camera, unstable background, ORB,
optical flow, clustering, Delaunay triangulation
1 Introduction
Detection of moving objects of interest and tracking of such objects from frame
to frame is an important tasks in systems that perfoms of video data, such as
video surveillance systems, industrial robots, unmanned vehicles etc.
Fig. 1. Moving Object Detection in Video Streams Received from a Moving Camera
(a)ORB feature points. (b)Classification of feature points, (c)Moving object in video
stream
Based on the type of motion between frames, all objects can be divided into
two classes: static and dynamic. Static objects maintain their position on the
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sequence of frames, dynamic objects change their position in space. There has
been considerable research focusing on the separation of static and dynamic
objects in video sequences taken from camera in stationary positions.
The classical object detection methods can not be applied directly for de-
tecting such objects in a scenario with a moving camera because there exist
multiple sources of motions from both the camera and the moving objects. In
our research we focus attention on the problem of moving object detection in
a video stream captured using a moving camera. To detect moving objects in
a moving camera environment we need to discriminate between camera motion
and object motions. Generally, there are three approaches to detect the moving
object under a moving camera:
• Compensation of camera motion by ego-motion estimation [1][2];
• Separation of motions vectors in the input sequence using motion models
[3][4];
• Segmentation of the camera and object motions using the graph cut algo-
rithm [5][6].
Some of these methods need an additional algorithmic stage to select the
moving object motion model, others require considerable computation time. We
decided to design an improved moving-object detection method using data from a
free-moving camera with a non-stationary background, which provides both high
detection performance and fast processing speed. In this paper we demonstrate
the proposed approach with initial results.
2 Motion estimation
For extracting structured information about the object of interest in the image,
we search for feature points of the scene.
A feature point in a scene M is called a scene point if it is coplanar with
other points in image neighborhood O(M ) that can be distinguished from all
other neighborhoods O(N ), which in turn are composed of several points N .
2.1 Feature Point Detection
In the proposed method, to find the feature points of the image, the Oriented
Fast and Brief detector (ORB) is used (Fig. 1 (a)). When using this technique,
it is assumed that the intensity of the corner point is offset from the center
and this displacement vector can be considered as feature point direction. To
calculate a descriptor of the point p(x, y), ORB compares brightness values of
points located in its vicinity. This algorithm is invariant to image rotation, scale
change, and changes in lighting level, so it satisfies the main qualities required
of robust feature detectors and is suitable for a reliable estimation of moving
singular points.
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2.2 Optical flow computation
To determine the movement of objects in two-dimensional space using optical
sensor systems, algorithms in computer vision and image processing make use
of optical flow - the apparent motion of the image, which is a shift of each point
between two consecutive frames.
In our approach, we compute the optical flow vectors of image points by
searching for the corresponding feature points between two consecutive image
frames using the pyramidal Lucas-Kanade method. This process consists of two
tasks: generation of image pyramid and search for the correspondence feature
points on the image pyramid.
3 Motion Clustering
Clustering is the division of the set of input vectors into groups (clusters) on the
degree of "similarity" to each other.
In this paper, we cluster feature points using the length L and direction
θ of optical flow vectors. The feature points are described in the optical flow
coordinate (L, θ).
All optical flow coordinate (L, θ) were divided into blocks. Randomly were
selected the initial points for clustering. Number of clusters is an input parameter
of the method. In the present implementation of the algorithm is assumed two:
background and foreground.
4 Motion classification
The clusters generated are to be separated into those that relate to the movement
of the camera and those to moving objects.
In the proposed framework, we assume that the background occupies a larger
area of the frame than moving objects. Thus, the points that relate to the back-
ground have a greater dispersion than singular points belonging to objects in
motion (except in cases where the background has a large amount of small de-
tails). The assignment of each cluster to a background or a moving object can be
done using the measure of spread of the points within the cluster. To determine
the measure of the spread of points within each cluster, in the present work, we
use the standard deviation s as a discriminative metric.
Cluster, which has the highest standard deviation, be deemed to apply to
the background (Fig. 1 (b)).
5 Moving object detection
To select the area that relates to a movable object, in the proposed framework,
Delaunay triangulation has been used. Triangulation is a planar partition of the
2D space by plane figures, one of which is an outer infinity, and the rest are
triangles.
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When using Delaunay triangulation, for all resulting triangles, points of the
cluster except for points at vertices lie outside the circle circumscribed about
the triangle.
After constructing the Delaunay triangulation, the resulting set of triangles
with edges length exceeding a predetermined threshold are removed (Fig. 1 (c)).
6 Conclusion
In this work, we have developed an effective method for separation of moving
objects in the scene using data from an input video stream in the presence of
a non-stationary background. This method shows high frame rate performance
- 20-21 fps on a computer with a processor Intel Xeon E5420 1333 MHz and
4GB RAM. However, this value does not satisfy the operation mode in real
time (>24 frames per second). In order to improve the real-time performance of
the algorithm, we envisage that in subsequent implementations, in addition to
algorithmic optimization, implementation will be carried out on a graphics card
using software optimization libraries for CUDA and OpenCL. Also planned are:
• Exploration of the possibility of introducing additional parameters to im-
prove the quality of clustering.
• Implementation of automatic identification of cluster numbers in the step
for clustering singular points.
• Implementation of automatic identification of cluster numbers in the step
for clustering singular points.
References
1. Hayman E., Eklundh J.: Statistical background subtraction for a mobile observer.
In: Proc. IEEE ICCV 2003. (2003)
2. Ren Y, Chua CS, Ho YK: Statistical background modeling for non-stationary cam-
era. Pattern Recogn 24(1-3) (2003) 183–196
3. Borshukov GD, Bozdagi G, Altunbasak Y, Tekalp AM: Motion segmentation by
multistage affine classidication. IEEE Trans Image Process 6(11) (1997) 1591–1594
4. Ke Q, Kanade T: A subspace approach to layer extraction. In: IEEE CVRP. (2001)
5. M, X.J.S.: Motion layer extraction in the presence of occlusion using graph cuts.
IEEE Trans Pattern Anal Mach Intell 27(10) (2005) 1644–1659
6. D, S.T.C.: High resolution motion layer decomposition using dualspace graph cuts.
In: In: Proc. IEEE CVPR. (2008)
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Выделение движущихся объектов сцены из
входящего видеопотока при наличии
нестационарного фона
Сергей Старков, Максим Лукьянченко
НИЯУ МИФИ, Обнинск, Россия
starkov@iate.obninsk.ru, maksim.lukyanchenko@gmail.com
Аннотация Автоматическое выделение движущихся объектов сце-
ны из входящего видеопотока – одна из важнейших задач анализа
изображений. За последнее время было предложено большое количе-
ство методов решающих данную задачу при условии неподвижности
видеокамеры. В основе этих методов лежит принцип накопления кад-
ров и выявления изменений в них. Однако при наличии подвижной
камеры применение данного подхода становится невозможным. Вме-
сте с тем развитие автономных беспилотных транспортных средств
требует решения и этой задачи компьютерного зрения. В представ-
ленной работе предложен метод нацеленный на решение указанной
задачи, в его основе лежит выделение ключевых точек изображения,
вычисление оптического потока, сегментация изображения на фон
и объекты относящиеся к переднему плана, маркирование участков
изображения. Произведена оценка результатов работы предложен-
ного метода.
Ключевые слова: движущийся объект, подвижная камера, неста-
бильный фон, оптический поток, кластеризация, триангуляция.
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