Uav (unmanned aerial vehicle) inspection and surveillance services
Vehicle Detection in Aerial Surveillance Using Dynamic Bayesian Networks
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Transcript of Vehicle Detection in Aerial Surveillance Using Dynamic Bayesian Networks
Vehicle Detection in Aerial Surveillance Using
Dynamic Bayesian NetworksHsu-Yung Cheng, Member, IEEE, Chih-Chia
Weng, and Yi-Ying ChenIEEE TRANSACTIONS ON IMAGE
PROCESSING, VOL. 21, NO. 4, APRIL 2012
Goal
Introduction
• These technologies have a variety of applications, such as military,police, and traffic management.
• Aerial surveillance is more suitable for monitoring fast-moving targets and covers a much larger spatial area.
Introduction
• Cheng and Butler [8] performed color segmentation via mean-shift algorithm and motion analysis via change detection.
• In [11], the authors proposed a moving-vehicle detection method based on cascade classifiers.
• Choi and Yang [12] proposed a vehicle detection algorithm using the symmetric property of car shapes.
Introduction
Background Color Removal
• quantize the color histogram bins as 16*16*16.
• Colors corresponding to the first eight highest bins are regarded as background colors and removed from the scene.
Feature Extraction:Local Feature Analysis
Feature Extraction:Local Feature Analysis
Feature Extraction:Local Feature Analysis
• After evaluation, is known.
• Use the gradient magnitude G(x,y) of each pixel of moment-preserving.
• Tmax =T ,Tmin=0.1*(Gmax-Gmin) for Canny edge detector.
• Harris detector is for the corners.
Feature Extraction:Color Transform and Color Classification
• In [16],they proposed a color domain (u,v) instead of (R,G,B) to separate vehicle and non-vehicle pixels clearily.
• Use n*m as a block to train SVM model to classify color.
Feature Extraction:Color Transform and Color Classification
Feature Extraction
• We extract five types of features, S,C,E,A and Z for the pixel.
• A=L/W• Z=blue counts at left
Dynamic Bayesian Network
• Use some videos to train the probabilities with people marked ground truth.
• Vt indicates if a pixel belongs to a vehicle.
• P(Vt|St) is defined as the probability that a pixel belongs to a vehicle pixel at time slice given observation St at time Instance t.
Experimental results
Experimental results
Experimental results
Experimental results
Experimental results
Experimental results
Experimental results