# Multi-Camera Person Re-Identification Based on Trajectory Data
## 4 Conclusions  
In this study, we developed a system for detecting and tracking customers in a commercial retail environment using videos captured with video surveillance cameras in a multi-camera environment. These videos are blurred in the area of customers' faces in order to keep their anonymization, making it difficult to use traditional image-based re-identification methods. Thus, this study proposes a mechanism for re-identifying people based on the paths they have traveled in the store. The trajectories are obtained based on information from the detection and tracking system which, after detecting and associating an identifier to each person, calculates and extracts the trajectory taken by them. The tests conducted show the effectiveness of the proposed trajectory-based re-identification mechanism in associating a unique identifier to each customer regardless of the camera they were detected in. Our method was capable of successfully re-identifying 82% of the customers present in the considered examples. This approach can be seen as a way to improve the accuracy of tracking algorithms, allowing them to minimize the number of identifiers generated, and presents an alternative to the use of image-based person re-identification processes in a commercial environment with multiple cameras. It is also a solution for dealing with some occlusions and errors caused by object detectors and tracking algorithms, a solution to consider when the data we have have part of the person occluded due to privacy issues or when we simply only have access to the trajectory data. As for future work, optimizing the re-identification process will require rigorous testing across varied datasets. Central to this line of work is the understanding that the accuracy of current detection and tracking processes lies in the precision of the algorithms employed. Therefore, exploring alternatives or enhancing the existing ones will be important to achieve superior results. Moreover, the context in which the cameras operate is very relevant. We anticipate that in environments with fewer occlusion challenges than our current dataset, the re-identification mechanism would obtain better results. Therefore, we expect that the strategies we provided in this work by incorporating occlusion-aware mechanisms will improve the trajectory quality by mitigating the errors arising from occlusions, as also discussed in [18]. Another suggestion is to add confidence scores to re-identifications, since they can offer a more nuanced perspective on their reliability. Finally, it is worth noting that our framework, in its current form, was not developed for real-time scenarios. However, with suitable modifications, transitioning it to real time is a feasible step. In conclusion, this work utilizes images obtained in a complex retail environment, with constraints such as occlusions and data privacy concerns, to re-identify customers in large retail stores. It contributes with a different approach to the topic of person re-identification in multi-camera environments since it only uses trajectory data to accomplish this re-identification and represents a possible alternative to the traditional methods based on images. Conceptualization, T.B., L.N., P.A. and P.J. ; methodology, T.B., L.N. and P.A. ; software, D.M. and S.C.; validation, D.M. ; investigation, D.M., P.J. and S.C.; resources, L.N. ; data curation, L.N. and D.M. ; writing--original draft preparation, D.M. ; writing--review and editing, L.N., T.B., P.A. and S.C.; visualization, D.M. ; supervision, L.N., T.B. and P.A. ; project administration, L.N. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was partially supported by Fundacao para a Ciencia e a Tecnologia, I.P. (FCT) [ISTAR Projects: UIDB/04466/2020 and UIDP/04466/2020] and P2020 project \"ECI4.0\": LISBOA-01-0247-FEDER-047155. **Institutional Review Board Statement:** Not applicable.