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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. Outline. Introduction Proposed vehicle detection framework Experimental Results Conclusion . Introduction.

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Vehicle Detection in Aerial Surveillance Using Dynamic Bayesian Networks

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  1. Vehicle Detection in Aerial Surveillance Using Dynamic Bayesian Networks Hsu-Yung Cheng, Member, IEEE, Chih-Chia Weng, and Yi-Ying Chen

  2. Outline • Introduction • Proposed vehicle detection framework • Experimental Results • Conclusion

  3. Introduction • These technologies have a variety of applications, such as military, police, and traffic management. • Cheng and Butler performed color segmentation via mean-shift algorithm and motion analysis via change detection.

  4. Introduction • Choi and Yang proposed a vehicle detection algorithm using the symmetricproperty of car shapes. • In this paper, we design a new vehicle detection framework that preserves the advantages of the existing works and avoids their drawbacks.

  5. Introduction

  6. Proposed vehicle detection framework • A. Background Color Removal • B. Feature Extraction • Local Feature Analysis • Color Transform and Color Classification • C. Dynamic Bayesian Network(DBN)

  7. Background Color Removal • Since nonvehicle regions cover most parts of the entire scene in aerial images. • We 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.

  8. Feature Extraction- Local Feature Analysis pj= nj/n • Tmax =T ,Tmin=0.1*(Gmax-Gmin) for Canny edge detector. • Harris detector is for the corners.

  9. Feature Extraction-Color Transform and Color Classification • A new color model transforms (R,G,B) color components into the color domain • (3) • (4)

  10. Feature Extraction-Color Transform and Color Classification • Use n*m as a block to train SVM model to classify color.

  11. Feature Extraction • (5) • (6) • (7) • A=Length/Width • Z is the pixel count of “vehicle color region 1”

  12. Dynamic Bayesian Network(DBN) • Use some videos to train the probabilities with people marked ground truth. • indicates if a pixel belongs to a vehicle at time slice t. (8)

  13. Post Processing • We use morphological operations to enhance the detection mask and perform connected component labeling to get the vehicle objects.

  14. Experimental results

  15. Experimental results

  16. Experimental results

  17. Experimental results results of color classification by SVM after background color removal and local feature analysis.

  18. Experimental results Fig. 11(a) shows the results obtained using the traditional Canny edge detector with nonadaptive thresholds. Fig. 11(b) shows the detection results obtained using the enhanced Canny edge detector with moment-preserving threshold selection.

  19. Experimental results

  20. Experimental results

  21. Experimental results

  22. Conclusion • The number of frames required to train the DBN is very small. • Overall, the entire framework does not require a large amount of training samples. • For future work, performing vehicle tracking on the detected vehicles can further stabilize the detection results.

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