# Margin CosReid Network for Pedestrian Re-Identification
## 5 Conclusions  
In this paper, we proposed a margin CosReid network for end-to-end pedestrian re-identification with respect to feature and metric-based learning combinations. On the one hand, the feature extraction model GBNeck was proposed to overcome the overfitting and slow convergence problems of traditional CNNs, which is a typical model of feature-based learning. On the other hand, the margin cosine softmax loss (MCSL) was proposed by introducing a boundary margin parameter that can maximize the difference between classes and minimize those within classes simultaneously. Our margin CosReid network can not only achieve a stronger generalization and more discriminative feature learning models, but can also deal with outside interferences strongly for the metric-based learning model. It was tested on Market-1501 and DukeMTMC-reID and had superior performance compared to state-of-the-art pedestrian re-identification methods in accuracy and robustness. Although a common phenomenon in deep network, the accuracy saturation problem of the proposed MCSL still affects the accuracy enhancement. In the future work, we plan to introduce a deep residual learning to decrease accuracy saturation and obtain an even higher accuracy. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Rank-1** & **Rank-5** & **Rank-10** & **mAP** \\ \hline LSRO [19] & 67.7 & - & - & 47.1 \\ APR [22] & 70.7 & - & - & 51.9 \\ PAN [59] & 71.6 & 83.9 & 90.6 & 51.5 \\ PNGAN [26] & 73.6 & - & - & 53.2 \\ SVDNet [52] & 76.7 & 86.4 & 89.9 & 56.8 \\ MaskReID [58] & 78.8 & - & - & 61.9 \\ PSE [50] & 79.8 & 89.7 & 92.2 & 62.0 \\ AWTL [56] & 79.8 & - & - & 63.4 \\ Deep KPM [57] & 80.3 & - & - & 63.2 \\ HA-CNN [60] & 80.5 & - & - & 63.8 \\ \hline MCSL & 81.1 & 90.6 & 93.3 & 64.6 \\ MCSL+RK & **82.9** & **91.5** & **94.2** & **73.4** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparisons of rank-1, rank-5, rank-10, and mAP with different methods on the DukeMTMC-reID dataset. Bold fonts denote the highest values. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Rank-1** & **Rank-5** & **Rank-10** & **mAP** \\ \hline PIE [51] & 78.7 & 90.3 & 93.6 & 53.9 \\ SVDNet [52] & 82.3 & 92.3 & 95.2 & 62.1 \\ LSRO [19] & 83.4 & - & - & 66.1 \\ PDC [53] & 84.1 & 92.7 & 94.9 & 63.4 \\ APR [22] & 84.3 & 93.2 & 95.2 & 64.7 \\ DML [54] & 87.7 & - & - & 68.8 \\ PSE [50] & 87.7 & 94.5 & 96.8 & 69.0 \\ Camstyle [55] & 88.1 & - & - & 68.7 \\ PNGAN [26] & 89.4 & - & - & 72.6 \\ AWTL [56] & 89.5 & - & - & 75.7 \\ Deep KPM [57] & 90.1 & - & - & 75.3 \\ MaskReID [58] & 90.4 & - & - & 75.4 \\ \hline MCSL+single-query & 91.0 & 96.6 & 98.0 & 75.9 \\ MCSL+RK & 92.6 & 95.8 & 97.1 & 87.9 \\ MCSL+multi-query & 93.8 & 97.9 & 98.8 & 83.0 \\ MCSL+RK+multi-query & **94.2** & **98.1** & **98.9** & **88.5** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparisons of rank-1, rank-5, rank-10, and mAP with different methods on the Market-1501 dataset. Bold fonts denote the highest values. **Author Contributions:** [PERSON] designed the study, analysed data, and wrote the paper and the revisions. [PERSON] performed the experiments and analysed data. [PERSON] contributed to refining the ideas, and is responsible for the research team. [PERSON] performed the experiments and analysed data. [PERSON] collected and analysed the data. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was funded by [the Natural Science Foundation of Jiangsu Province] grant number [BK20180640], [the National Natural Science Foundation of China] grant number [61902404, 512918914, 62001475, 51804304, 6177417, 51734009, 61873246], and [the State Key Research Development Program] grant number [2016 YFC0801403]. **Data Availability Statement:** Not applicable.