Research on high-definition video
vehicles location and tracking
Xiong Changzhen , LiLin
IEEE, Distributed Computing and Applications to
Business Engineering and Science (DCABES),
2010 Ninth International Symposium on
1、 Introduction
• high-definition(HD) video detection system
can achieve high-definition monitoring to
urban roads.
– Managers can analyze clearly the road traffic
condition and traffic flow vehicle character.
• With the capability of intelligent analysis to
high-definition images, it can achieve road
traffic flow detection and incident detection.
1、 Introduction
• The existing vehicle detection algorithm of
standard-definition(SD) video mainly includes:
–
–
–
–
–
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Optical flow method
Frame difference method
Edge detection method
Gaussian background modeling method
Bayesian background modeling method
Background modeling based on codebook method
• this paper presents a vehicle tracking method
based on image brightness.
2、HD (high-definition) video vehicle
detection algorithm
• The road can be divided into the lane area
– Because interference between the adjacent lanes
is relatively small.
– Taking lane as a unit.
• Lane detection can also be used to adaptively
demarcate detection area.
• The video with the resolution 2592 * 1936 is
used in this experiment
2.1 Interest Region
2.2 Brightness Curve
Scanning lanes by line along the vertical direction in an image,
summing the brightness value in line.
2.2 Brightness Curve
• Reflect the complete information
– such as lane width, vehicle size, vehicle color
(black or white), moving distance of vehicles in the
image, elapsed time, etc.
1. Circle out a region in the grayscale image
according to the direction of traffic flow
2. Denoise the stored values (removing the
values which is too large or too small)
3. Draw the curve.
2.3 Vehicle Location
Input image
• Take the absolute value
Is initial background?
after difference with the
background value.
no
yes
• Adaptive Gaussian
Pre-processing
background modeling method
Adaptive Background
production
-
Background
Background update
2.3 Vehicle Location
• Adaptive Background Extraction:
d>T =>mark as uncertain background
d<=T =>background
Background updating
others
2.3 Vehicle Location
• we can get vehicle curve, as shown in the
following formula
2.3 Vehicle Location
convex part means the vehicle's location, and we can get that the
number of vehicles in this frame in this lane is 2.
2.4 Vehicle Tracking
• Count the number of vehicles. The match is
taking upward position in our video
• if frame i +1 has surplus vehicles in addition to
them matching vehicles, then the surplus vehicles
are new vehicles, number them from top to
bottom
• Number rules: number frame i from top to
bottom in turn, the top vehicle is 1, the following
vehicle is 2,3 • • •, the new vehicle in frame i +1
is numbered in succession
• Do the same processing to another two lanes
EXPERIMENTAL
RESULTS
• The video is taken by
AV5105 5 Mega pixel
IP-camera high-definition
camera .
CPU is Intel 1.83G
dual-core Dell desktop
EXPERIMENTAL RESULTS