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Real-Time Surveillance and ATD for Search and Rescue

Real-Time Surveillance and ATD for Search and Rescue. May 22, 2008 Planning meeting James Elder, York University. Overview of Recent Work. Database Development Static Target Detection Motion Stabilization Moving Target Detection Combining Static and Moving Target Detectors Denoising.

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Real-Time Surveillance and ATD for Search and Rescue

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  1. Real-Time Surveillance and ATD for Search and Rescue May 22, 2008 Planning meeting James Elder, York University

  2. Overview of Recent Work • Database Development • Static Target Detection • Motion Stabilization • Moving Target Detection • Combining Static and Moving Target Detectors • Denoising

  3. Datasets Ground dataset: people and land vehicles Maritime dataset: watercraft

  4. Ground Dataset Ground Dataset 1 1 0.9 0.9 0.8 0.8 0.7 0.7 0.6 0.6 0.5 0.5 Hit Rate Hit Rate 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 0 0 0 0 5 5 10 10 15 15 20 20 False Alarms per Frame False Alarms per Frame Static Detection Algorithms on Ground Dataset

  5. Static Detection Algorithms on Maritime Dataset ROC of Maritime Dataset 1 0.9 0.8 0.7 0.6 Hit Rate 0.5 0.4 0.3 Pixel Classification 0.2 Pixel Posterior Summation Normal IID 0.1 Zero Mean Normal Model Human 0 0 2 4 6 8 10 12 14 16 18 20 False Alarms per Frame

  6. Train on Ground, Test on Maritime Train on Ground, Test on Maritime 1 Pixel Classification Pixel Posterior Summation 0.8 Normal IID Model Zero-Mean Normal Model 0.6 Hit Rate 0.4 0.2 0 0 5 10 15 20 False Alarms per Frame

  7. Train on Maritime, Test on Ground Train on Maritime, Test on Ground 1 Pixel Classification Pixel Posterior Summation 0.8 Normal IID Model Zero Mean Normal Model 0.6 Hit Rate 0.4 0.2 0 0 5 10 15 20 False Alarms per Frame

  8. SIFT Features for Motion Correspondence

  9. Two-Frame Residuals

  10. Performance MSE MSE Lucas Lucas - - Kanade Kanade SIFT SIFT Direct Direct No Transformation No Transformation

  11. Motion Detection Algorithms on Ground Dataset ROC for Motion Detection on Ground Dataset ROC for Motion Detection on Ground Dataset 1 1 0.9 0.9 0.8 0.8 0.7 0.7 0.6 0.6 Hit Rate Hit Rate 0.5 0.5 0.4 0.4 Pixel Classification Pixel Classification 0.3 0.3 Pixel Posterior Summation Pixel Posterior Summation Normal IID Normal IID 0.2 0.2 Multivariate Normal Multivariate Normal 0.1 0.1 Zero Mean Normal Zero Mean Normal Human Human 0 0 0 0 5 5 10 10 15 15 20 20 False Alarms per Frame False Alarms per Frame

  12. Combining static and motion detection Ground Dataset -4 x 10 0.8 6 Target Target Background Background 0.6 4 Probability Probability 0.4 2 0.2 0 0 -2 0 2 4 0 2 4 6 8 10 Matched filter response Matched filter response 4 x 10 Motion Detector Static Detector

  13. Results (Ground Dataset) ROC for Ground Dataset 1 0.8 0.6 Hit Rate 0.4 Motion Detector Static Detector 0.2 Combined Detector Human 0 0 5 10 15 20 False Alarms per Frame

  14. Mean Filter Median Filter 15.3 dB 14.8 dB Unregistered 17.9 dB 17.5 dB Registered Temporal Denoising

  15. Conclusions • Databases • Targets may not be realistic for search and rescue • Ground-truthing valuable • Motion Stabilization • Classic Lucas & Kanade method found to work well • ATD • Algorithms limited by single-scale model • Static: best was zero-mean normal • Motion: best was multivariate normal • Combined: sparsity of motion results in no improvement

  16. Conclusions (cntd…) • Denoising • Useful in low SNR conditions • Motion stabilization found to improve results • Assumed noise-free registration (results may be optimistic).

  17. Possible Future Work (Main Ideas) • Generalize detectors to be multi-scale • Develop methods for ‘cold-target’ detection • Develop database by embedding SAREX targets into appropriate video imagery • Equalize luminance and contrast of targets with background • Use transparency to simulate partial occlusion • Develop methods based upon local contour statistics • Compare against human performance for same data (vary rarity of targets) • Study psychophysics of cueing

  18. Possible Future Work (Additional Ideas) • Analyze misses/false alarms from prior experiments to gain insights • Develop tracking/temporal integration methods to improve detection rates and allow change detection • Empirically determine optimal frame delay for motion detection

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