# Joint Object Detection and Multi-Object Tracking Based on Hypergraph Matching
## 5 Experiments
### Benchmark Evaluation  
To demonstrate the advantages of our hypergraph-based architecture in the MOT task, we have selected many pioneering and influential methods for comparison. The compared methods are divided into online tracking and offline tracking. We have summarized the MOTChallenge results of different methods in Tables 2-4. For the aforementioned methods, if the performance on the corresponding dataset is reported on the official leaderboard, we adopt it directly. If not, we compare it based on the latest data provided in the selected papers. In most metrics, the proposed method has achieved good performance on most evaluation metrics. Since the MOTA metric emphasizes the detection side more, we believe that our strong HOTA performance indicates that our detector and data association modules perform well. In addition, our impressive IDF1 performance indicates that, due to the spatiotemporal relationship modeling through hypergraph interactions, our method can produce temporally consistent tracklets. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **MOTA** & **HOTA** & **IDF1** & **FP** & **FN** & **IDS** \\ \hline GMTracker [3] & 61.1 & 51.2 & 66.6 & 3891 & 66,550 & 503 \\ MPNTrack [6] & 58.6 & 48.9 & 61.7 & 4949 & 70,252 & 354 \\ DeepMOT [29] & 54.8 & 42.2 & 53.4 & 2955 & 78,765 & 645 \\ LPC [27] & 58.8 & 51.7 & 67.6 & 6167 & 68,432 & 435 \\ Lif\_T [59] & 57.5 & 49.6 & 64.1 & 4249 & 72,868 & 335 \\ TMOH [60] & 63.2 & 50.7 & 63.5 & 3122 & 63,376 & 635 \\ UTM [61] & 63.8 & 53.1 & 67.1 & 8328 & 57,269 & 428 \\ JDTHM & 67.6 & 56.3 & 69.1 & 6137 & 52,686 & 377 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison with different MOT methods on MOT16 benchmark with the provided public detections, showing the quantitative results. Among the existing methods, the one most related to us is GMTracker [3], which uses a similar graph matching method for identity association. The main differences and novelties lie in our use of knowledge extracted from historical tracklets to enhance the object detection and feature embedding modules. On the MOT16 dataset, our proposed method has improved by 6.5%, 5.1%, and 2.5% in MOTA, HOTA, and IDF1 metrics, respectively. On the MOT17 dataset, compared to GMTracker [3], our proposed method has improved by 14.9%, 8.5%, and 5.8% in MOTA, HOTA, and IDF1 metrics, respectively. Particularly, on the MOT20 dataset, we also compared with the JDE method GSDT [43], and our proposed method has improved by 2.2%, 3.6%, and 3.4% in MOTA, HOTA, and IDF1 metrics, respectively. A positive feedback loop that fuses historical tracklet features is used for enhancement and correction. At the same time, compared to the second-order information features used in other MOT methods based on graph neural networks, our method can not only extract higher-order information features but also pay more attention to the visual consistency relationship of occluded tracking objects, thereby helping the tracker to associate better in severe occlusion situations. Another point worth mentioning is that our IDS is better than most mainstream methods; the smaller the IDS, the fewer the number of identity switchovers in the tracking tracklet and the higher the reliability of the tracking results. Figure 5 displays the qualitative analysis results of our hypergraph architecture on the MOT17.