Learning to Associate: HybridBoosted Multi-Target Tracker for Crowded Scene

1. Learning to Associate: HybridBoosted Multi-Target Tracker for Crowded Scene Present by 陳群元

2. review

3. Association • First round • input • tracklet association • lkis the number of tracklets in Sk. • corresponding trajectory of Sk • trackletassociation set.

4. MAP problem

5. How to associate • Bruce force?

6. Hungarian Algorithm(1) • Arrange your information in a matrix with the "people" on the left and the "activity" along the top, with the "cost" for each pair in the middle.

7. Hungarian Algorithm(2) • Ensure that the matrix is square by the addition of dummy rows/columns if necessary.

8. Hungarian Algorithm(3) • Reduce the rows by subtracting the minimum value of each row from that row.

9. Hungarian Algorithm(4) • Reduce the columns by subtracting the minimum value of each column from that column.

10. Hungarian Algorithm(5) • Cover the zero elements with the minimum number of lines it is possible to cover them with.

11. Hungarian Algorithm(6) • Add the minimum uncovered element to every covered element.

12. Hungarian Algorithm(7) • Subtract the minimum element from every element in the matrix.

13. Hungarian Algorithm(8) • Cover the zero elements again. If the number of lines covering the zero elements is not equal to the number of rows, return to step 6.

14. Hungarian Algorithm(9) • Select a matching by choosing a set of zeros so that each row or column has only one selected.

15. Hungarian Algorithm(10) • Apply the matching to the original matrix, disregarding dummy rows. This shows who should do which activity, and adding the costs will give the total minimum cost.

17. Time Complexity Time consuming 資料量 (number of response)

18. Data Partition • 整段影片(2359frame)：３hr • 切割時間／空間(8x8)：３min • 時間(8x8) • 空間(200frame)

19. Feature

20. demo

21. To do • Human detection • Build ground truth • Post processing

22. Thank you for your attention!