Vehicles Detection From Aerial Sequences 4 th European Micro-UAV Meeting

1. Vehicles Detection From Aerial Sequences 4th European Micro-UAV Meeting 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 1

2. 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 2

3. 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 3

4. 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 4

5. 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 5

6. 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 6

7. 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 7

8. Vehicles Detection From Aerial Sequences 4th European Micro-UAV Meeting Approch based on perceptual (geometrical) organization University of Picardie Jules Verne 8

9. 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 : • Parallelism • Proximity University of Picardie Jules Verne 9

10. Vehicles Detection From Aerial Sequences 4th European Micro-UAV Meeting Approch based on perceptual (geometrical) organization University of Picardie Jules Verne 10

11. 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 11

12. Vehicles Detection From Aerial Sequences 4th European Micro-UAV Meeting 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) ? Results Verifying ? University of Picardie Jules Verne 12

13. 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 ? • Corners matching process is less complicated than edges matching process • Corners rate repeatability is more elevated than edges rate repeatability • Edge displacement is computed as the mean of corners displacements, so false matching effects are reduced University of Picardie Jules Verne 13

14. 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 14

15. 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 15

16. 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 16

17. , 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 17

18. 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 18

19. 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 19

20. 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 20

21. Vehicles Detection From Aerial Sequences 4th European Micro-UAV Meeting THANK YOU ! University of Picardie Jules Verne 21