## 5 Conclusions  
In this paper, having in view navigation tasks in assistive mobile robot platforms, an evaluation study of two MOTs by detection algorithms, SORT and Deep-SORT, was presented. Moreover, eight new tracking data association metrics based on intersection over union, Euclidean distances, and bounding boxes ratio were proposed. To evaluate both tracking methods with the proposed data association metrics, the ISR Tracking dataset, which represents the object conditions from an assistive mobile robot's point of view, was also proposed. The presented pipeline consists of using the YOLOv3 network to detect and classify the objects available on RGB images, feeding the tracking algorithm. Promising results were attained by the majority of the proposed tracking data association metrics on the SORT, and also on the Deep-SORT. Overall, based on the performed experiments, the SORT method was able to achieve higher results of accuracy and precision, while the Deep-SORT method obtained the best values of FN, IDs, and MT. Moreover, the proposed \(A\) data association metric achieved the best performance on both evaluated object tracking methods. The \(A\) data association metric showed a significant improvement on the MT evaluation metric, which could be crucial to successful navigation tasks on robotic platforms. The results showed, as expected, that the object tracking overall performance has a high dependency on the object detector performance. The SORT is faster than the Deep-SORT, reaching 50 FPS on the overall pipeline (YOLOv3 + SORT). Therefore, considering navigation tasks in assistive platforms, and also considering issues associated with an object detector algorithm, the SORT method using the \(A\) data association metric obtained more robust results and, as such, can be a more suitable approach.  
As future work, it is intended to integrate the presented pipeline on the RobChair [21] platform for assistive navigation tasks.  
Conceptualization, methodology, software, validation, investigation: R.P. and G.C.; formal analysis, R.P., G.C. and L.G.; writing, R.P., G.C., L.G. and U.J.N.; supervision, funding acquisition: U.J.N. All authors have read and agreed to the published version of the manuscript.  
[PERSON] has been supported by the Portuguese Foundation for Science and Technology (FCT) under a PhD grant with reference SFRH/BD/148779/2019. This work has been also supported by the projects B-RELABLE with reference SAICT/30935/2017 and MATIS-CENTRO-01-0145-FEDER-000014. It was also partially supported by ISR-UC through FCT grant UIDB/00048/2020.  
Not applicable.  
Data available in a publicly accessible repository. Publicly available datasets were analyzed in this study. Data can be found here: [[https://motchallenge.net/data/MOT1](https://motchallenge.net/data/MOT1)]([https://motchallenge.net/data/MOT1](https://motchallenge.net/data/MOT1)) 7 (accessed on 17 October 2021) and [[https://github.com/rmca16/ISR_RGB-D_Dataset](https://github.com/rmca16/ISR_RGB-D_Dataset)]([https://github.com/rmca16/ISR_RGB-D_Dataset](https://github.com/rmca16/ISR_RGB-D_Dataset)) (accessed on 20 December 2021).  
The authors declare no conflict of interest.