zhao, h., et al., detection and tracking of moving objects
TRANSCRIPT
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MONITORING AN INTERSECTION USINGA NETWORK OF LASER SCANNERS
H. Zhao, J. Cui, H. Zha,Peking University
K. Katabira, X. Shao, R. Shibasaki,University of Tokyo
Zhao, H., et al., Detection and Tracking of Moving Objects at Intersections using a Network of Laser Scanners, IEEE Trans. ITS, vol. 13, no.2, 655-670, 2012.
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Background (1)
Analyzing and Monitoring the traffic behavior in an intersection
Efficiently and accurately COLLECTING the TRAFFICDATA in an INTERSECTION
Real-timely DETECTINGDANGEOUS SITUATIONS.
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Background (2)• Vision-based methods suffer mainly on the
following difficulties– Occlusion– Computation Cost– Illumination Change
1. Restrict camera’s setting condition
e.g. the camera is required to be set on a tall position, monitoring intersection from the above
e.g. monitor vehicles of limited lanes, do not discriminate moving objects.
To solve the problems
2. Target on a simplified situation
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ObjectiveThis research propose a novel system for monitoring and collecting detailed traffic data, with easy setting condition, in an environment of complicated traffic behavior, such as intersection, using a network of single-row laser scanner.
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System ImageServer PC
#2
#1
Laser Scanner
Pedes.
#1
#2
Car
Car
Bicycle
Network
An image of integrated laser data
Laser Scanner
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LD1LD3
LD2
LD2
LD3
LD1
Integrated frame Single laser frame
Single laser frameSingle laser frame
Safety zone
Safety zone
Road side
Road side
10m
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Processing ModulesServer
Data Integration
Detection
Tracking
Recognition
Moving object
Measure Data
BG SubtractionBG Generation
Clustering
Client Measure Data
BG SubtractionBG Generation
Clustering
…Laser Scanner 1 Laser Scanner N
Calibration, Synchronization
Network
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Difficulty 1
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The data of the same object might be spatially disconnected
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Difficulties
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The data of different objects might be spatially close
There are different kinds of objects, complicated motions
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Object Model
(v3, sv3)
len1
len2p (v2, sv2)(v4, sv4)
(v1, sv1)
(c, val) u
u
(v1=dirv)
dirv
Feature parametersand their reliabilities
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d3
d2
d1
d4
d5
d6d7
c2
c1
c3c4
c5
c6c7
Object Detection Results
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c1 d1
c4
d4
c5
d5
c6
d6
c7
d7
c2
c3
d2
d3
Object Detection Results
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The previous frame
The current frame
More sensors bring more confusion in data association
Difficulty 2
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Peking Univ.
Peking Univ.
Sports Center
Overhead Bridge
Laser Scanner
Experiment
Video Camera
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40cm
激光扫描仪
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D34 T31
Results
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Results
D28 T29
objects are occluded
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Accuracy Analysis2007.10.13, 10:00-10:20
Detection Results
Tracking Results
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Trajectory Clustering Result
Straight:
Left Turn:
Moving Direction
Right Turn:
U Turn:
Cars
Trajectory Clustering
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Trajectory Clustering Result
Straight:
Crossing:
Moving Direction
Turn along road:
Bicycle
Trajectory Clustering
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Trajectory Clustering Result
Pattern 1:
Pattern 2:
Moving Direction
Pattern 3:
Pedestrians
Trajectory Clustering
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Path Model
ijμ i
jνr
ijσ
Pd
),0(~)( ij
ij
ij NPd σνμ
r⋅−=
},...,2,1|),,{( jij
ij
ijj ni ==Γ σνμr
Each cross-section is represented by a GaussianA path is model as a sequence of Gausians
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Trajectory Evaluation
Likelihood between a trajectory and a path is evaluated
jΓkT
)|( jkTP Γ ∏=
Γ∝kL
pj
pkTP
1
)|(
∏=
Γ=kL
p
ij
pkTP
1
' )|(
2'2 2'
'
21)|(
ijd
ij
ij
pk eTP σ
σπ−=Γ
// is a trajectory pointpkT
'ijΓ// is the nearest cross-section to p
kT
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Trajectory ClassificationGiven a trajectory , the objective is to classify it with in
as
where
and
can be either 0 or 1. if and only if
*T},,...,,{ 21 anclass ΓΓΓΓ=
)|(argmax ** TP Γ=ΓΓ
)()|(1)|( ** aaa PTPTP ΓΓ=Γη
}1,0{,))|(()|(1
** ∈<Γ=Γ ∏=
n
iiia TPTP ε
)()|(1)|( ** iii PTPTP ΓΓ=Γη
)|( * aTP Γ
1)|( * =ΓaTP iiTPi ε<Γ∀ )|( *
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Path models
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Example 1: Normal Trajectory Example 2: Abnormal Trajectory
Example 3: Abnormal Trajectory Example 4: Abnormal Trajectory
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Real Abnormal Trajectory
frame 11241 frame 11392
frame 11616 frame 12366
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Real Abnormal Trajectory
frame 12090 frame 12765
frame 13590 frame 13965frame 13215
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False Abnormal Trajectoryframe 3367frame 3142
frame 3704frame 3554
frame 4417frame 3910frame 3817
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Thank You!
赵卉菁(Huijing Zhao) Tel: 010 62757458Email: [email protected]://www.cis.pku.edu.cn/faculty/vision/zhaohj/
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