Learning to Associate: HybridBoosted Multi-Target Tracker

for Crowded Scene

Present by 陳群元

review

Association

• First round• input• tracklet association

– lk is the number of tracklets in Sk.

• corresponding trajectory of Sk

• tracklet association set.

MAP problem

How to associate

• Bruce force?

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.

Hungarian Algorithm(2)

• Ensure that the matrix is square by the addition of dummy rows/columns if necessary.

Hungarian Algorithm(3)

• Reduce the rows by subtracting the minimum value of each row from that row.

Hungarian Algorithm(4)

• Reduce the columns by subtracting the minimum value of each column from that column.

Hungarian Algorithm(5)

• Cover the zero elements with the minimum number of lines it is possible to cover them with.

Hungarian Algorithm(6)

• Add the minimum uncovered element to every covered element.

Hungarian Algorithm(7)

• Subtract the minimum element from every element in the matrix.

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.

Hungarian Algorithm(9)

• Select a matching by choosing a set of zeros so that each row or column has only one selected.

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.

Time Complexity

Tim

e consuming

資料量(number of response)

Data Partition

• 整段影片 (2359frame) ：３ hr• 切割時間／空間 (8x8) ：３ min

– 時間 (8x8)– 空間 (200frame)

Feature

demo

To do

• Human detection • Build ground truth• Post processing

Thank you for your attention!