# IOF-Tracker: A Two-Stage Multiple Targets Tracking Method Using Spatial-Temporal Fusion Algorithm
## 4 Conclusions  
In this paper, we propose the IOF-Tracker, a two-stage multi-object tracking method that integrates spatial and temporal feature fusion, aiming to enhance tracking accuracy and robustness by combining static spatial positions with dynamic temporal motion. To reduce the impact of noise on optical flow information, we estimate the temporal features of the target using the average optical flow of each pixel. We represent the spatial information using the R-IoU value between the target detection box and the predicted box. By fusing temporal and spatial features and applying them to a two-stage association tracking algorithm, the tracker can fully utilize the spatiotemporal features of the target for association matching during the tracking process, thereby reducing the occurrence of ID switches due to target occlusion and interaction in multi-object tracking. The robustness of optical flow vectors in estimating subtle movements across consecutive frames also contributes to high scores in the tracking benchmark IDF1. In practical applications, such as autonomous driving or intelligent security scenarios, it is only necessary to replace the corresponding dataset and train the detector so that it can be used for the detection stage of the tracking targets. This allows the IOF-Tracker multi-object tracking method to perform real-time \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Tracker** & **MOTA \(\uparrow\)** & **IDF1 \(\uparrow\)** & **HOTA \(\uparrow\)** & **FP \(\downarrow\)** & **FN \(\downarrow\)** & **IDs \(\downarrow\)** \\ \hline UTM [20] & 63.5 & 65.1 & 52.5 & 33,683 & 170,352 & 1686 \\ CenterTrack [21] & 67.8 & 64.7 & 52.2 & 18,498 & 160,332 & 3039 \\ SOTOM [22] & 71.0 & 71.9 & - & 39,537 & 118,983 & 5184 \\ TransCenter [23] & 73.2 & 62.2 & 54.5 & 23,112 & 123,738 & 4614 \\ FairMOT [24] & 73.7 & 72.3 & 59.3 & 27,507 & 117,477 & 3303 \\ SiamMOT [25] & 76.3 & 72.3 & - & - & - & - \\ TransMOT [26] & 76.7 & 75.1 & 61.7 & 36,231 & 93,150 & 2346 \\ OCSORT [27] & 78.0 & 77.5 & 63.2 & **15,129** & 107,055 & 1950 \\ StrongSORT [28] & 78.3 & 78.5 & 63.5 & 27,876 & 86,205 & 1446 \\ ByteTrack [3] & 78.9 & 77.2 & 62.8 & 25,491 & 83,721 & 2196 \\ FeatureSORT [29] & 79.6 & 77.2 & 63.0 & 29,588 & 83,132 & 2269 \\ Ours & **80.5** & **79.9** & **64.9** & 27,245 & **81,653** & **1370** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results on MOT17 challenge test set (\(\uparrow\): The higher the parameter, the better. \(\downarrow\): The lower the parameter, the better. Bold font: indicates the best value in that column). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Tracker** & **MOTA \(\uparrow\)** & **IDF1 \(\uparrow\)** & **HOTA \(\uparrow\)** & **FP \(\downarrow\)** & **FN \(\downarrow\)** & **IDs \(\downarrow\)** \\ \hline MLT [30] & 48.9 & 54.6 & 43.2 & 45,660 & 216,803 & 2187 \\ FairMOT [24] & 61.8 & 67.3 & 54.6 & 103,440 & 88,901 & 5243 \\ TransCenter [23] & 61.9 & 50.4 & - & 45,895 & 146,347 & 4653 \\ SiamMOT [25] & 67.1 & 69.1 & - & - & - & - \\ SOTMOT [22] & 68.6 & 71.4 & - & 57,064 & 101,154 & 4209 \\ OCSORT [27] & 75.7 & **76.3** & 62.4 & 19,067 & 105,894 & 942 \\ ByteTrack [3] & 75.7 & 74.9 & 60.9 & 26,249 & **87,594** & 1223 \\ StrongSORT [28] & 72.2 & 75.9 & 61.5 & **16,632** & 117,920 & **770** \\ FeatureSORT [29] & 76.6 & 75.1 & 61.3 & 25,083 & 95,027 & 1081 \\ Ours & **77.7** & 75.0 & **62.0** & 25,019 & 88,959 & 1530 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results on MOT20 challenge test set (\(\uparrow\): The higher the parameter, the better. \(\downarrow\): The lower the parameter, the better. Bold font: indicates the best value in that column). multi-object tracking in different application scenarios. Although this method can address tracking issues due to occlusion to some extent, especially in sparsely populated scenes where it performs exceptionally well, in densely populated scenes, frequent interactions between targets can affect the stability of the optical flow field, potentially leading to less than ideal tracking performance.