## 4 Experiment
### The Performance of Tracking-by-Detection Algorithm Compared with Other Algorithms
\begin{table}
\begin{tabular}{l l r r r r r r r r} \hline \hline
**Sequence** & **Algorithm** & **MOTA** & **IDF1** & **MOTP** & **MT** & **ML** & **FP** & **FN** & **ID Sw** \\ \hline \multirow{4}{*}{TUD-Crossing} & MDP\_Subcnn & 78.9 & 74.5 & 76.7 & 0.692 & 0 & 32 & 195 & 6 \\  & DMT & 70 & 56.3 & 73.3 & 0.615 & 0 & 73 & 229 & 29 \\  & Sort & 67.5 & 57.6 & 74.5 & 0.462 & 0.077 & 32 & 308 & 18 \\  & PFS(our) & 74.6 & 49.9 & 76.2 & 0.615 & 0 & 49 & 193 & 38 \\ \hline \multirow{4}{*}{PETS09-S2L2} & MDP\_Subcnn & 47.5 & 36.8 & 72.6 & 0.071 & 0.095 & 341 & 4524 & 196 \\  & DMT & 47.7 & 36.8 & 70.4 & 0.214 & 0.19 & 502 & 4429 & 113 \\  & Sort & 27.4 & 23.6 & 67.4 & 0 & 0.262 & 806 & 5958 & 240 \\  & PFS(our) & 56.7 & 32 & 74.4 & 0.19 & 0.048 & 358 & 3519 & 295 \\ \hline \multirow{4}{*}{ETH-Jelmoli} & MDP\_Subcnn & 48.2 & 65.7 & 77.3 & 0.356 & 0.222 & 492 & 814 & 9 \\  & DMT & 48.2 & 62.8 & 75 & 0.6 & 0.111 & 758 & 529 & 26 \\  & Sort & 39 & 52.9 & 74.1 & 0.2 & 0.289 & 439 & 1071 & 38 \\  & PFS(our) & 61.5 & 59 & 76.2 & 0.356 & 0.267 & 176 & 754 & 46 \\ \hline \multirow{4}{*}{ETH-Linthescher} & MDP\_Subcnn & 63.9 & 67.1 & 72.1 & 0.244 & 0.31 & 495 & 2657 & 70 \\  & DMT & 60.5 & 60.2 & 76.4 & 0.437 & 0.244 & 1425 & 1963 & 138 \\  & Sort & 52.2 & 54.5 & 73.8 & 0.147 & 0.406 & 397 & 3725 & 144 \\  & PFS(our) & 62.8 & 57.5 & 76.8 & 0.335 & 0.223 & 735 & 2374 & 215 \\ \hline \multirow{4}{*}{ETH-Crossing} & MDP\_Subcnn & 63.8 & 76.5 & 79.5 & 0.192 & 0.269 & 64 & 293 & 6 \\  & DMT & 59 & 54.7 & 81.5 & 0.308 & 0.308 & 169 & 221 & 21 \\  & Sort & 55.4 & 49.7 & 80.3 & 0.154 & 0.385 & 58 & 368 & 21 \\  & PFS(our) & 62.4 & 53.9 & 79.5 & 0.308 & 0.077 & 135 & 215 & 27 \\ \hline \multirow{4}{*}{AVG-TownCentre} & MDP\_Subcnn & 49.5 & 64.5 & 70.1 & 0.389 & 0.155 & 1381 & 2106 & 121 \\  & DMT & 45.5 & 54.6 & 68.5 & 0.283 & 0.274 & 653 & 3127 & 117 \\  & Sort & 27.2 & 45.1 & 67.4 & 0.058 & 0.279 & 1111 & 3930 & 162 \\  & PFS(our) & 47.5 & 59.7 & 69.5 & 0.372 & 0.115 & 1483 & 2094 & 178 \\ \hline \multirow{4}{*}{ADL-Rundle-1} & MDP\_Subcnn & 33.4 & 49.9 & 72.4 & 0.344 & 0 & 2899 & 3230 & 70 \\  & DMT & 26 & 42.2 & 70.1 & 0.219 & 0.281 & 1697 & 5146 & 47 \\  & Sort & 20.3 & 31.5 & 72.5 & 0.188 & 0.375 & 1493 & 5812 & 108 \\  & PFS(our) & 39.8 & 38.6 & 73.8 & 0.25 & 0.094 & 1444 & 3998 & 164 \\ \hline \multirow{4}{*}{ADL-Rundle-3} & MDP\_Subcnn & 44.9 & 51.6 & 79.6 & 0.205 & 0.159 & 793 & 4752 & 56 \\  & DMT & 43.3 & 45.4 & 75.4 & 0.295 & 0.136 & 1168 & 4517 & 84 \\  & Sort & 37.4 & 43.4 & 77 & 0.295 & 0.182 & 1498 & 4765 & 99 \\  & PFS(our) & 34.8 & 34.1 & 80 & 0.273 & 0.159 & 2012 & 4422 & 197 \\ \hline \multirow{4}{*}{KITTI-16} & MDP\_Subcnn & 50 & 66.6 & 70.3 & 0.353 & 0.059 & 262 & 566 & 22 \\  & DMT & 44.7 & 60.5 & 69.3 & 0.235 & 0 & 232 & 690 & 19 \\ \cline{1-1}  & Sort & 34.6 & 42.8 & 70.1 & 0.118 & 0.059 & 1444 & 938 & 30 \\ \cline{1-1}  & PFS(our) & 50 & 56.7 & 73.2 & 0.235 & 0.059 & 268 & 544 & 39 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The performance of proposed tracking-by-detection algorithm compared with other algorithms in 2 DMOT2015 dataset. The green and yellow colors indicate the best and the second best algorithm in each measure. It can be seen that the proposed algorithm has high accuracy. The actual effect of the algorithm on the MOT2015 dataset is shown in Figure 8.