# Multi-Object Tracking with Grayscale Spatial-Temporal Features
## 5 Conclusions  
We propose a novel method on tracking via traditional methods, which are more suitable to run on monitors than deep learning methods. Compared to deep learning methods, our method has very low requirements for the computing power. Using this  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Module** & **MOTA \(\uparrow\)** & **IDF1 \(\uparrow\)** & **MT \(\uparrow\)** & **ML \(\downarrow\)** & **TFPS \(\uparrow\)** \\ \hline MotionTrack [9] (private) & 81.1 & 80.1 & - & - & - \\ FairMOT [26] (private) & 68.7 & 70.4 & 39.5 & 19.0 & - \\ MOTRv2 [21] (private) & 78.6 & 75.0 & - & - & - \\ \hline STAM16 [27] & 46.0 & 50.0 & 14.6 & 43.6 & 0.2 \\ DMAN [28] & 46.1 & **54.8** & 17.4 & 42.7 & 0.3 \\ DASOT [29] & 46.1 & 49.4 & 14.6 & 41.6 & 9.0 \\ TAMA [30] & 46.2 & 49.4 & 14.1 & 44.0 & 6.3 \\ DM [31] & 46.5 & 52.3 & - & 35.6 & 10.1 \\ AMIR [32] & **47.2** & 46.3 & 14.0 & 41.6 & 1.0 \\ Ours & 46.7 & 52.0 & **21.5** & **34.1** & **17.6** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of multi-object tracking methods on MOT16. \(\uparrow\) indicates that higher is better, \(\downarrow\) indicates that lower is better. The best results are shown in **bold**.  
Figure 6: Examples of long-term occlusion. The top row is the visualization result of the tracker on MOT16-09. The bottom row is the visualization result of the tracker on MOT16-02.  
method, we obtain target spatial-temporal features and region spatial-temporal features by grayscale mapping. Our spatial-temporal features are simple and fast to match between two frames, and we reduce matching times by more than three times for most targets by using these two spatial-temporal features. This can reduce unnecessary ID matching, which solves the problem of taking some time to calculate when matching IDs. Our proposed method is based on traditional methods, so our approach does not require data to train. Especially for the tracking-by-detection paradigm, if using our method, it will not only reduce some feature extraction calculations but also reduce some unnecessary ID matching. Our method maintains or even improves tracking accuracy while reducing the computations, as demonstrated on the MOT16 benchmark set. For future works, first, we need to further reduce the number of computations while ensuring accuracy. We will study how to effectively extract the appearance features of the target through the histogram without using the yolov5 seg cutting function. Second, during the night, motion features and grayscale spatiotemporal features can still be used, but the appearance features extracted by a color histogram are useless. Therefore, we will study how to extract effective appearance features from infrared cameras in the night. We hope that our work can help MOT research to achieve real-time performance.  
Conceptualization, L.X. and G.W.; methodology, L.X. and G.W.; software, L.X.; validation, L.X.; formal analysis, L.X. and G.W.; investigation, L.X.; resources, L.X.; data curation, L.X.; writing--original draft preparation, L.X.; writing--review and editing, L.X.; visualization, L.X.; supervision, G.W.; project administration, G.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available in MOT16 at [[https://motchallenge.net/data/MOT16/](https://motchallenge.net/data/MOT16/)]([https://motchallenge.net/data/MOT16/](https://motchallenge.net/data/MOT16/)), accessed on 1 July 2022, reference number [7]. These data were derived from the following resources available in the public domain: [[https://motchallenge.net/data/MOT16/](https://motchallenge.net/data/MOT16/)]([https://motchallenge.net/data/MOT16/](https://motchallenge.net/data/MOT16/)), accessed on 1 July 2022.  
The authors declare no conflicts of interest.