4th European Micro-UAV Meeting
Vehicles Detection From Aerial Sequences
VEHICLES DETECTION FROM AERIAL
SEQUENCES
Center of Robotics, Electrical engineering and Automatic - EA3299
University of Picardie Jules Verne
CREA, 7 rue du moulin neuf 80000 Amiens, France
&
Diagnosis and Advanced Vehicles (DIVA) Pole
Conseil Régional de Picardie
University of Picardie Jules Verne
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Vehicles Detection From Aerial Sequences
4th European Micro-UAV Meeting
Aerial sequences Analysis taken from an
UAV-Camera system
Proposed approaches aim to extract and
recognize vehicles in the road
University of Picardie Jules Verne
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Vehicles Detection From Aerial Sequences
4th European Micro-UAV Meeting
Using computer vision tools => A large basis of information
A whole description of the traffic :
•Vehicle counts
•Vehicle speed
•Vehicle density
•Flow rates … etc.
Road traffic monitoring :
•Congestion and incident detection
•Law enforcement
•Automatic vehicle tracking… etc.
University of Picardie Jules Verne
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Vehicles Detection From Aerial Sequences
4th European Micro-UAV Meeting
Computer vision systems for road traffic monitoring :
•Static vision system : fixed camera
•Dynamic vision system : moving camera
University of Picardie Jules Verne
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Vehicles Detection From Aerial Sequences
4th European Micro-UAV Meeting
Static vision system => Fixed background :
Approaches farm the difference between acquired
images and background
Moving vehicles are ,so, extracted
University of Picardie Jules Verne
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Vehicles Detection From Aerial Sequences
4th European Micro-UAV Meeting
Dynamic vision system : Camera-UAV system
Having a fixed background is impossible
University of Picardie Jules Verne
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Vehicles Detection From Aerial Sequences
4th European Micro-UAV Meeting
We propose two approaches to extract vehicles :
•Approch based on perceptual (geometrical) organization
•Approch based on « common fate » principle
University of Picardie Jules Verne
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Vehicles Detection From Aerial Sequences
4th European Micro-UAV Meeting
Approch based on perceptual
(geometrical) organization
University of Picardie Jules Verne
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Vehicles Detection From Aerial Sequences
4th European Micro-UAV Meeting
Approch based on perceptual
(geometrical) organization
A graph problem where :
•
Nodes are images edges.
•
Links based on two criteria :
1. Parallelism
2. Proximity
University of Picardie Jules Verne
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Vehicles Detection From Aerial Sequences
4th European Micro-UAV Meeting
Approch based on perceptual
(geometrical) organization
University of Picardie Jules Verne
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Vehicles Detection From Aerial Sequences
4th European Micro-UAV Meeting
Approch based on « common fate »
Principle
Sequences taken from an UAV-camera system
Two types of movement :
•
objects movement or displacement (in our
case : edges presenting vehicles)
•
background movement.
The idea : distingush between these two kinds of
movement
University of Picardie Jules Verne
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4th European Micro-UAV Meeting
Vehicles Detection From Aerial Sequences
Approch based on « common fate »
Principle
Corners Detection : Image(t),Image(t+1)
Primitives Detection : Image(t)
Primitives Description
Matching
Displacements Computation
Homogeneous Primitives Extraction
rank(W) ?
University of Picardie Jules Verne
Results Verifying ?
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Vehicles Detection From Aerial Sequences
4th European Micro-UAV Meeting
Approch based on « common fate »
Principle
Why do we use corners data to matching images edges ?
1.
Corners matching process is less complicated than edges
matching process
2.
Corners rate repeatability is more elevated than edges rate
repeatability
3.
Edge displacement is computed as the mean of corners
displacements, so false matching effects are reduced
University of Picardie Jules Verne
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Vehicles Detection From Aerial Sequences
4th European Micro-UAV Meeting
Approch based on « common fate »
Principle
Matching tool : Computing of the Mahalanobis
distances between corners invariants vectors.
University of Picardie Jules Verne
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Vehicles Detection From Aerial Sequences
4th European Micro-UAV Meeting
Approch based on « common fate »
Principle
Homogeneous Primitives Extraction ?
A graph problem where :
•
Nodes are images edges
•
Links are nodes displacements similarity
University of Picardie Jules Verne
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Vehicles Detection From Aerial Sequences
4th European Micro-UAV Meeting
Approch based on « common fate »
Principle
Partitioning tool : Normalized cuts technique
Link between two nodes (edges) i and j :
University of Picardie Jules Verne
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Vehicles Detection From Aerial Sequences
4th European Micro-UAV Meeting
Approch based on « common fate »
Principle
Verifying Algorithm : The Dempster Shafer Theory
Verifying system has 5 input sensors
and 3 output degrees
,
University of Picardie Jules Verne
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Vehicles Detection From Aerial Sequences
4th European Micro-UAV Meeting
Approch based on « common fate »
Principle
V = 79,95 %
Conflict = 1 %
University of Picardie Jules Verne
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Vehicles Detection From Aerial Sequences
4th European Micro-UAV Meeting
Approch based on « common fate »
Principle
NR : Number of Rejected
classifications before
converging
University of Picardie Jules Verne
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Vehicles Detection From Aerial Sequences
4th European Micro-UAV Meeting
Approch based on « common fate »
Principle
NR : Number of Rejected
classifications before
converging
University of Picardie Jules Verne
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Vehicles Detection From Aerial Sequences
4th European Micro-UAV Meeting
THANK YOU !
University of Picardie Jules Verne
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