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Vehicle Detection in Aerial Surveillance Using Dynamic Bayesian Networks

Vehicle Detection in Aerial Surveillance Using Dynamic Bayesian Networks. Hsu-Yung Cheng , Member, IEEE, Chih-Chia Weng, and Yi-Ying Chen IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 21, NO. 4, APRIL 2012. Goal. Introduction.

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Vehicle Detection in Aerial Surveillance Using Dynamic Bayesian Networks

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  1. Vehicle Detection in Aerial Surveillance UsingDynamic Bayesian Networks Hsu-Yung Cheng, Member, IEEE, Chih-Chia Weng, and Yi-Ying Chen IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 21, NO. 4, APRIL 2012

  2. Goal

  3. 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.

  4. 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.

  5. Introduction

  6. 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.

  7. Feature Extraction:Local Feature Analysis

  8. Feature Extraction:Local Feature Analysis

  9. 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.

  10. 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.

  11. Feature Extraction:Color Transform and Color Classification

  12. 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

  13. 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.

  14. Experimental results

  15. Experimental results

  16. Experimental results

  17. Experimental results

  18. Experimental results

  19. Experimental results

  20. Experimental results

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