Target Recognition

1. Target Recognition Harmatz Isca Supervisor: Nakhmani Arie Semester: Winter 2007

2. Project goals • Create a target classification system based on dimension reduction, using the targets contour. • No dependence on illumination and color • Universal method works on all target types and sizes • Fast learning for new targets • Low computational needs • The dimension reduction algorithm can be adopted to work on all types of data.

3. Motivation • Tracking people • ATR– automatic target recognition • Find suspects in given areas • Look for specific characteristics of targets

4. Method Change Detection Snakes Dimension Reduction Post processing Result

5. Working Database • 475 images • 2176 snakes found • The snakes were divided into 3 types: • Real (339) – a snake of a person • Partial (155) – a snake were the person was partially hidden, or a clear silhouette was not detected • False (1682) – a snake of a random change in the image

6. Change detection Create average reference image = Background Image Subtract the background from the image Detect changes in image Get several reference images Find changed pixels

7. Level Set Evolution Without Re-initialization: A New Variational Formulation Chunming Li, Chenyang Xu, Changfeng Gui, and Martin D. Fox Snakes CVPR 2005

8. X Y Database 14 18 21 33 45 30 25 20 16 15 15 22 25 40 46 70 73 29 20 10 10 23 25 40 46 70 71 25 20 11 25 58 36 96 46 71 24 46 28 81 20 24 77 82 67 13 26 69 32 14 41 25 58 59 53 67 94 31 37 76 19 73 97 13 64 28 79 14 82 39 96 32 59 63 15 57 56 95 42 73 74 03 24 26 23 21 81 89 46 56 58 14 32 56 87 46 28 81 83 57 74 58 25 14 36 95 45 67 21 32 85 14 36 95 74 82 24 65 46 81 74 85 35 69 21 14 25 49 67 58 54 68 89 45 21 34 25 19 47 76 14 18 21 33 45 30 25 20 16 15 15 22 25 40 46 70 73 29 20 10 74 25 84 51 86 53 71 28 25 26 82 39 74 14 16 49 82 34 49 86 43 62 LLE or PCA 53 14 48 25 21 54 Dimension reduction • Select target snake • Transform snake to vector • Add snake vector to vector database • Perform dimension reduction on vectors • Displaying dimension reduction results in graph

9. Local Linear Embedding (LLE) For every snake in database: • Find K nearest neighbors { z1:K } • Find weight Wij for every neighbor zj • Compute the projection to lower space where weighted distance from neighbors is minimum

10. Principal components analysis (PCA) • Calculate the covariance matrix of database • Calculate eigenvectors (ordered by eigenvalues) • Find snakes representation with eigenvectors 0.51 + 0.12 + 0.3 + 0.07

11. LLE Non-linear embedding Local Keeps subspace with best local linear structure Assumes local linearity PCA Linear embedding Global Keeps subspace with best variance of data Assumes global linearity LLE vs PCA

12. Results LLE

13. Results PCA

14. Post-processing • Steps taken to achieve better separation between false and true snakes • Compactness: Area/Perimeter² • Adaptive Database • Target Tracking

15. Compactness Grade = area/perimeter2

16. Dimension Reduction and Compactness Grade = GradePCA. GradeCompactness

17. Adaptive Database • Unsupervised • Snakes matching a certain grade level are added to the database. Snakes in database with low grades are removed. • The algorithm was applied for every movie separately

18. Adaptive Database

19. Tracking • Define Target of interest • For every next image: • Define search region • If “good” snake is found, then Set target to found snake • Else Increase search area • Move to next image

20. Tracking Results

21. Conclusions • Dimension reduction was used to find people in images. • The method works well on clear silhouettes. • Different post-processing methods used to improve results, each with its own pros and cons. • The method works with a small database (20 snakes) and can be adopted for real time work.

22. Feature Directions • Occluded target support • Improve target tracking • Multiple targets • Kalman / Particle filters • Target specific database • Adaptive grade threshold • Improved snakes