報告人 : 林福城 指導老師 : 陳定宏 1 From Res. Center of Intell. Transp. Syst., Beijing...
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Transcript of 報告人 : 林福城 指導老師 : 陳定宏 1 From Res. Center of Intell. Transp. Syst., Beijing...
![Page 1: 報告人 : 林福城 指導老師 : 陳定宏 1 From Res. Center of Intell. Transp. Syst., Beijing Univ. of Technol., Beijing, China By Zhe Liu ; Yangzhou Chen ; Zhenlong Li Appears.](https://reader036.fdocument.pub/reader036/viewer/2022062517/56649ee15503460f94bf13aa/html5/thumbnails/1.jpg)
報告人 :林福城指導老師 :陳定宏
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From Res. Center of Intell. Transp. Syst., Beijing Univ. of Technol., Beijing, ChinaBy Zhe Liu ; Yangzhou Chen ; Zhenlong Li Appears in: Computer Science and Information Engineering, 2009 WRI World Congress on
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Outline1.Introduction2.Moving object detection 2-1.Conscutive image difference 2-2.Backgrout difference3.Moving object tracking 3-1.Review of Tracking Algorithm 3-2.Camshift Multiple Vehicle Tracking4.Traffic Parameters Estimation5.Experimental Results6.Conclusions
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1.IntroductionTraffic management and information systems: 1.Inductive loop detectors 2.Visual surveillance systems
Our approach specifies three sub process: 1. Vehicle Extraction : Consecutive image difference Background difference 2. Vehicle Tracking 3. Traffic Parameter Estimation
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2.1 Consecutive Image Difference
D(x,y) is the difference image.Mask(x,y) is the image after binarization.
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2.2 Background Difference(1)
It assume a moving objectwould not stay at the same position for more than half of n frames.
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2.2 Background Difference(2)
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2.Moving Object Detection Conclusion 1.Consecutive 2.Background
Easily realized Good
The change of scene luminance
Good
Extract precise Good
Process time Good
After morphological process Equal Equal
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3-1.Review of Tracking Algorithm1.Tracking based on a moving object region:
Size, Color, Shape, Velocity, Centroid2.Tracking based on an active contour of a moving
object3.Tracking based on a moving object model4.Tracking based on selected features of moving
objects : Corner
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3-2.CamShift Multiple Vehicle Tracking
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Mean-Shift Object TrackingGeneral Framework: Target Localization
Search in the model’s neighborhoo
d in next frame
Start from the position of the model
in the current frame
Find best candidate by maximizing a similarity
func.
Repeat the same process
in the next pair of frames
Current frame
… …Model Candidat
e
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Mean-Shift Object TrackingTarget Representation
Choose a reference
target model
Quantized Color Space
Choose a feature space
Represent the model by
its PDF in the feature
space
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
1 2 3 . . . m
color
Pro
bab
ility
Kernel Based Object Tracking, by Comaniniu, Ramesh, Meer 11
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Mean-Shift Object TrackingPDF Representation
,f y f q p y Similarity
Function:
Target Model(centered at 0)
Target Candidate(centered at y)
1..1
1m
u uu mu
q q q
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
1 2 3 . . . m
color
Pro
bab
ility
1..
1
1m
u uu mu
p y p y p
0
0.05
0.1
0.15
0.2
0.25
0.3
1 2 3 . . . m
color
Pro
bab
ility
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4.Traffic Parameters Estimation1.Vehicle count2.Vehicle average speed3.Vehicle size
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Experimental Results
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ConclusionsIn this paper, we have presented methods for
detecting and tracking multiple vehicles in an outdoor environment. Each detected vehicle is assigned a camshift tracker which can effectively track object with different size and shape under different illumination conditions.
The method fails to handle long slow moving vehicle queue.
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