Vehicles Detection From Aerial Sequences 4 th European Micro-UAV Meeting University of Picardie Jules Verne 1 VEHICLES DETECTION FROM AERIAL SEQUENCES Center of Robotics, Electrical engineering and Automatic - EA3299 University of Picardie Jules Verne CREA, 7 rue du moulin neuf Amiens, France & Diagnosis and Advanced Vehicles (DIVA) Pole Conseil Régional de Picardie

Vehicles Detection From Aerial Sequences 4 th European Micro-UAV Meeting University of Picardie Jules Verne 2 Aerial sequences Analysis taken from an UAV-Camera system Proposed approaches aim to extract and recognize vehicles in the road

Vehicles Detection From Aerial Sequences 4 th European Micro-UAV Meeting University of Picardie Jules Verne 3 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.

Vehicles Detection From Aerial Sequences 4 th European Micro-UAV Meeting University of Picardie Jules Verne 4 Computer vision systems for road traffic monitoring : Static vision system : fixed camera Dynamic vision system : moving camera

Vehicles Detection From Aerial Sequences 4 th European Micro-UAV Meeting University of Picardie Jules Verne 5 Static vision system => Fixed background : Approaches farm the difference between acquired images and background Moving vehicles are,so, extracted

Vehicles Detection From Aerial Sequences 4 th European Micro-UAV Meeting University of Picardie Jules Verne 6 Dynamic vision system : Camera-UAV system Having a fixed background is impossible

Vehicles Detection From Aerial Sequences 4 th European Micro-UAV Meeting University of Picardie Jules Verne 7 We propose two approaches to extract vehicles : Approch based on perceptual (geometrical) organization Approch based on « common fate » principle

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

Vehicles Detection From Aerial Sequences 4 th European Micro-UAV Meeting University of Picardie Jules Verne 9 A graph problem where : Nodes are images edges. Links based on two criteria : 1.Parallelism 2.Proximity Approch based on perceptual (geometrical) organization

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

Vehicles Detection From Aerial Sequences 4 th European Micro-UAV Meeting University of Picardie Jules Verne 11 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

Vehicles Detection From Aerial Sequences 4 th European Micro-UAV Meeting University of Picardie Jules Verne 12 Corners Detection : Image (t),Image (t+1) Primitives Detection : Image (t) Primitives Description Matching Displacements Computation Homogeneous Primitives Extraction Results Verifying ? rank(W) ? Approch based on « common fate » Principle

Vehicles Detection From Aerial Sequences 4 th European Micro-UAV Meeting University of Picardie Jules Verne 13 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 Approch based on « common fate » Principle

Vehicles Detection From Aerial Sequences 4 th European Micro-UAV Meeting University of Picardie Jules Verne 14 Matching tool : Computing of the Mahalanobis distances between corners invariants vectors. Approch based on « common fate » Principle

Vehicles Detection From Aerial Sequences 4 th European Micro-UAV Meeting University of Picardie Jules Verne 15 Homogeneous Primitives Extraction ? A graph problem where : Nodes are images edges Links are nodes displacements similarity Approch based on « common fate » Principle

Vehicles Detection From Aerial Sequences 4 th European Micro-UAV Meeting University of Picardie Jules Verne 16 Approch based on « common fate » Principle Partitioning tool : Normalized cuts technique Link between two nodes (edges) i and j :

Vehicles Detection From Aerial Sequences 4 th European Micro-UAV Meeting University of Picardie Jules Verne 17 Approch based on « common fate » Principle Verifying Algorithm : The Dempster Shafer Theory Verifying system has 5 input sensors and 3 output degrees,

Vehicles Detection From Aerial Sequences 4 th European Micro-UAV Meeting University of Picardie Jules Verne 18 Approch based on « common fate » Principle V = 79,95 % Conflict = 1 %

Vehicles Detection From Aerial Sequences 4 th European Micro-UAV Meeting University of Picardie Jules Verne 19 Approch based on « common fate » Principle NR : Number of Rejected classifications before converging

Vehicles Detection From Aerial Sequences 4 th European Micro-UAV Meeting University of Picardie Jules Verne 20 Approch based on « common fate » Principle NR : Number of Rejected classifications before converging

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