# Person Re-Identification across Data Distributions Based on General Purpose DNN Object Detector
## 6 Conclusions and Future Work  
This paper has shown that a general-purpose DNN object detector, like the YOLO model, can address the person re-identification problem. The main goal was to implement a method that would achieve re-identification with the minimum requirements for computing and hardware resources.  
Our proposed method treats the re-identification as a classification problem, while the second part generalizes it. In this way, we only added five new layers to be trained. The small number of supplementary layers leads to a high processing speed, reducing both the time and resources required. We aimed to design a system that runs in real-time and generates as few computational costs as possible, but it would lead to a high re-identification rate.  
When considering all of these requirements, our proposed method manages to implement the first version of a general re-identification system. After testing a database utterly different from the one used in the learning stage, we obtained a Rank-1 re-identification rate of 50% and a Rank-20 of 72% (based on F-score function). This is a promising result in generalizing re-identification and achieving a robust and invariant system for variations of data distributions. Furthermore, besides recognizing a large number of people from an entirely new database, our algorithm is fast, providing the right balance between the speed, cost, and re-identification rate.  
Regarding future work, we intend to implement an improved version of the system that will increase the re-identification rate, keeping speed, and additional costs to a minimum. We aim to obtain a fully automatic re-identification system, containing a stage of detection of persons before the stage of re-identification.  
In conclusion, this paper demonstrates that it is possible to implement a re-identification system that runs on a wide range of real-life scenarios. It is possible to obtain a high re-identification rate on distribution without collecting large volumes of data from security systems or retrain the network while using a limited number of samples during the training stage. While, historically, person re-identification methods were dependent on a specific database, our paper shows that a system can still be usable on other distributions with minimal effort, even if the accuracy still needs some improvement.  
Conceptualization, R.-E.M. and S.C.; methodology, M.G.; software, R.-E.M. and M.C.; validation, C.P.; formal analysis, M.C. and S.C.; investigation, R.-E.M.; supervision, C.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported by grants of the Romanian Ministry of Education and Research, CNCS - UEFISCDI, project number: PN-III-P1-1.1-TE-2019-0420, project number: PN-III-P2-2.1-PTE-2019-0055, and project number PN-III-P2-2.1-PTE-2019-0570.  
The authors declare no conflict of interest.  
Figure 14: Comparison between the proposed method and another method based on deep metric embedding learning; (**a**) Comparison in terms of running time; (**b**) Comparison in terms of precision.