{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T18:34:19Z","timestamp":1777487659742,"version":"3.51.4"},"reference-count":43,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,1,26]],"date-time":"2022-01-26T00:00:00Z","timestamp":1643155200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia","doi-asserted-by":"publisher","award":["SFRH\/BD\/148779\/2019"],"award-info":[{"award-number":["SFRH\/BD\/148779\/2019"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia","doi-asserted-by":"publisher","award":["SAICT\/30935\/2017"],"award-info":[{"award-number":["SAICT\/30935\/2017"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia","doi-asserted-by":"publisher","award":["UIDB\/00048\/2020"],"award-info":[{"award-number":["UIDB\/00048\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Ag\u00eancia para o Desenvolvimento e Coes\u00e3o","award":["MATIS-CENTRO-01-0145-FEDER-000014"],"award-info":[{"award-number":["MATIS-CENTRO-01-0145-FEDER-000014"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Sciences"],"abstract":"<jats:p>Multi-Object Tracking (MOT) techniques have been under continuous research and increasingly applied in a diverse range of tasks. One area in particular concerns its application in navigation tasks of assistive mobile robots, with the aim to increase the mobility and autonomy of people suffering from mobility decay, or severe motor impairments, due to muscular, neurological, or osteoarticular decay. Therefore, in this work, having in view navigation tasks for assistive mobile robots, an evaluation study of two MOTs by detection algorithms, SORT and Deep-SORT, is presented. To improve the data association of both methods, which are solved as a linear assignment problem with a generated cost matrix, a set of new object tracking data association cost matrices based on intersection over union, Euclidean distances, and bounding box metrics is proposed. For the evaluation of the MOT by detection in a real-time pipeline, the YOLOv3 is used to detect and classify the objects available on images. In addition, to perform the proposed evaluation aiming at assistive platforms, the ISR Tracking dataset, which represents the object conditions under which real robotic platforms may navigate, is presented. Experimental evaluations were also carried out on the MOT17 dataset. Promising results were achieved by the proposed object tracking data association cost matrices, showing an improvement in the majority of the MOT evaluation metrics compared to the default data association cost matrix. In addition, promising frame rate values were attained by the pipeline composed of the detector and the tracking module.<\/jats:p>","DOI":"10.3390\/app12031319","type":"journal-article","created":{"date-parts":[[2022,1,26]],"date-time":"2022-01-26T11:02:53Z","timestamp":1643194973000},"page":"1319","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":87,"title":["Sort and Deep-SORT Based Multi-Object Tracking for Mobile Robotics: Evaluation with New Data Association Metrics"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6672-5395","authenticated-orcid":false,"given":"Ricardo","family":"Pereira","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Institute of Systems and Robotics, University of Coimbra, 3030-290 Coimbra, Portugal"}]},{"given":"Guilherme","family":"Carvalho","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Institute of Systems and Robotics, University of Coimbra, 3030-290 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3833-3794","authenticated-orcid":false,"given":"Lu\u00eds","family":"Garrote","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Institute of Systems and Robotics, University of Coimbra, 3030-290 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7750-5221","authenticated-orcid":false,"given":"Urbano J.","family":"Nunes","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Institute of Systems and Robotics, University of Coimbra, 3030-290 Coimbra, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.neucom.2019.11.023","article-title":"Deep learning in video multi-object tracking: A survey","volume":"381","author":"Ciaparrone","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Xu, Y., Osep, A., Ban, Y., Horaud, R., Leal-Taixe, L., and Alameda-Pineda, X. (2020, January 14\u201319). How To Train Your Deep Multi-Object Tracker. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00682"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Kamal, R., Chemmanam, A.J., Jose, B., Mathews, S., and Varghese, E. (2020, January 20\u201322). Construction Safety Surveillance Using Machine Learning. Proceedings of the International Symposium on Networks, Computers and Communications (ISNCC), Montreal, QC, Canada.","DOI":"10.1109\/ISNCC49221.2020.9297198"},{"key":"ref_4","unstructured":"Behrendt, K., Novak, L., and Botros, R. (June, January 29). A Deep Learning Approach to Traffic Lights: Detection, Tracking, and Classification. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Singapore."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1707","DOI":"10.1177\/0278364910365417","article-title":"Object Detection and Tracking for Autonomous Navigation in Dynamic Environments","volume":"29","author":"Ess","year":"2010","journal-title":"Int. J. Robot. Res."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Lo, S., Yamane, K., and Sugiyama, K. (2019, January 4\u20138). Perception of Pedestrian Avoidance Strategies of a Self-Balancing Mobile Robot. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China.","DOI":"10.1109\/IROS40897.2019.8968191"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1581","DOI":"10.1177\/0278364919881683","article-title":"Person-following by autonomous robots: A categorical overview","volume":"38","author":"Islam","year":"2019","journal-title":"Int. J. Robot. Res."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Wang, Q., Zhang, L., Bertinetto, L., Hu, W., and Torr, P.H. (2019, January 16\u201320). Fast Online Object Tracking and Segmentation: A Unifying Approach. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00142"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"76489","DOI":"10.1109\/ACCESS.2019.2921975","article-title":"Real-Time Online Multi-Object Tracking in Compressed Domain","volume":"7","author":"Liu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Bewley, A., Ge, Z., Ott, L., Ramos, F., and Upcroft, B. (2016, January 25\u201328). Simple online and realtime tracking. Proceedings of the IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA.","DOI":"10.1109\/ICIP.2016.7533003"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Wojke, N., Bewley, A., and Paulus, D. (2017, January 17\u201320). Simple Online and Realtime Tracking with a Deep Association Metric. Proceedings of the IEEE International Conference on Image Processing (ICIP), Beijing, China.","DOI":"10.1109\/ICIP.2017.8296962"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Chen, L., Ai, H., Zhuang, Z., and Shang, C. (2018, January 23\u201327). Real-Time Multiple People Tracking with Deeply Learned Candidate Selection and Person Re-Identification. Proceedings of the IEEE International Conference on Multimedia and Expo (ICME), San Diego, CA, USA.","DOI":"10.1109\/ICME.2018.8486597"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Pereira, R., Gon\u00e7alves, N., Garrote, L., Barros, T., Lopes, A., and Nunes, U.J. (2020, January 15\u201317). Deep-Learning based Global and Semantic Feature Fusion for Indoor Scene Classification. Proceedings of the IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), Ponta Delgada, Portugal.","DOI":"10.1109\/ICARSC49921.2020.9096068"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Pereira, R., Garrote, L., Barros, T., Lopes, A., and Nunes, U.J. (October, January 27). A Deep Learning-based Indoor Scene Classification Approach Enhanced with Inter-Object Distance Semantic Features. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, Czech Republic.","DOI":"10.1109\/IROS51168.2021.9636242"},{"key":"ref_15","unstructured":"Tan, M., and Le, Q.V. (2019, January 9\u201315). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. Proceedings of the International Conference on Machine Learning, Long Beach, CA, USA."},{"key":"ref_16","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv."},{"key":"ref_17","unstructured":"Zhang, Y., Wang, C., Wang, X., Zenf, W., and Liu, W. (2020). A Simple Baseline for Multi-Object Tracking. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Bertinetto, L., Valmadre, J., Henriques, J.F., Vedaldi, A., and Torr, P.H. (2016, January 11\u201314). Fully-convolutional siamese networks for object tracking. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-48881-3_56"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wu, J., Cao, J., Song, L., Wang, Y., Yang, M., and Yuan, J. (2021, January 20\u201325). Track to Detect and Segment: An Online Multi-Object Tracker. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01217"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Bergmann, P., Meinhardt, T., and Leal-Taix\u00e9, L. (2019, January 16\u201320). Tracking without bells and whistles. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/ICCV.2019.00103"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1109\/MRA.2016.2605403","article-title":"A New Hybrid Motion Planner: Applied in a Brain-Actuated Robotic Wheelchair","volume":"23","author":"Lopes","year":"2016","journal-title":"IEEE Robot. Autom. Mag."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"614","DOI":"10.1109\/TRO.2009.2020347","article-title":"A Noninvasive Brain-Actuated Wheelchair Based on a P300 Neurophysiological Protocol and Automated Navigation","volume":"25","author":"Iturrate","year":"2009","journal-title":"IEEE Trans. Robot."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1109\/THMS.2020.3047597","article-title":"A Self-Paced BCI With a Collaborative Controller for Highly Reliable Wheelchair Driving: Experimental Tests with Physically Disabled Individuals","volume":"51","author":"Cruz","year":"2021","journal-title":"IEEE Trans. Hum. Mach. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.robot.2012.11.002","article-title":"Assisted navigation for a brain-actuated intelligent wheelchair","volume":"61","author":"Lopes","year":"2013","journal-title":"Robot. Auton. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Sinyukov, D.A., and Padir, T. (2018, January 1\u20135). A Novel Shared Position Control Method for Robot Navigation Via Low Throughput Human-Machine Interfaces. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain.","DOI":"10.1109\/IROS.2018.8593921"},{"key":"ref_26","unstructured":"Carvalho, G. (2021). Kalman Filter-Based Object Tracking Techniques for Indoor Robotic Applications. [Master\u2019s Dissertation, University of Coimbra]."},{"key":"ref_27","unstructured":"Milan, A., Leal-Taix\u00e9, L., Reid, I.D., Roth, S., and Schindler, K. (2016). MOT16: A benchmark for multi-object tracking. arXiv."},{"key":"ref_28","unstructured":"Pereira, R., Garrote, L., Barros, T., Lopes, A., and Nunes, U.J. (September, January 31). An Experimental Study of the Accuracy vs Inference Speed of RGB-D Object Recognition. Proceedings of the IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), Naples, Italy."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3309665","article-title":"Handcrafted and Deep Trackers: Recent Visual Object Tracking Approaches and Trends","volume":"52","author":"Fiaz","year":"2019","journal-title":"ACM Comput. Surv."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1089","DOI":"10.1007\/s00371-020-01854-0","article-title":"Online multi-object tracking with pedestrian re-identification and occlusion processing","volume":"37","author":"Zhang","year":"2021","journal-title":"Vis. Comput."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Guo, Q., Feng, W., Zhou, C., Huang, R., Wan, L., and Wang, S. (2017, January 22\u201329). Learning Dynamic Siamese Network for Visual Object Tracking. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.196"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"843","DOI":"10.1109\/TAC.1979.1102177","article-title":"An algorithm for tracking multiple targets","volume":"24","author":"Reid","year":"1979","journal-title":"IEEE Trans. Autom. Control."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"He, J., Huang, Z., Wang, N., and Zhang, Z. (2021, January 20\u201325). Learnable Graph Matching: Incorporating Graph Partitioning with Deep Feature Learning for Multiple Object Tracking. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00526"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1002\/nav.3800020109","article-title":"The Hungarian method for the assignment problem","volume":"2","author":"Kuhn","year":"1955","journal-title":"Nav. Res. Logist. Q."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"8181","DOI":"10.1109\/ACCESS.2018.2889442","article-title":"Multiple Object Tracking via Feature Pyramid Siamese Networks","volume":"7","author":"Lee","year":"2019","journal-title":"IEEE Access"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Lin, T., Doll\u00e1r, P., Girshick, R.B., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature Pyramid Networks for Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Jin, J., Li, X., Li, X., and Guan, S. (2020, January 10\u201312). Online Multi-object Tracking with Siamese Network and Optical Flow. Proceedings of the IEEE 5th International Conference on Image, Vision and Computing (ICIVC), Beijing, China.","DOI":"10.1109\/ICIVC50857.2020.9177480"},{"key":"ref_38","unstructured":"Lucas, B., and Kanade, T. (1981, January 24\u201328). An Iterative Image Registration Technique with an Application to Stereo Vision. Proceedings of the 7th International Joint Conference on Artificial Intelligence (IJCAI), Vancouver, BC, Canada."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1007\/s12555-014-0353-4","article-title":"RGB-D Sensor-based Visual Target Detection and Tracking for an Intelligent Wheelchair Robot in Indoors Environments","volume":"13","author":"Xiao","year":"2015","journal-title":"Int. J. Control Autom. Syst."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Lecrosnier, L., Khemmar, R., Ragot, N., Decoux, B., Rossi, R., Kefi, N., and Ertaud, J.Y. (2021). Deep Learning-Based Object Detection, Localisation and Tracking for Smart Wheelchair Healthcare Mobility. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph18010091"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zheng, L., Bie, Z., Sun, Y., Wang, J., Su, C., Wang, S., and Tian, Q. (2016, January 11\u201314). MARS: A Video Benchmark for Large-Scale Person Re-identification. Proceedings of the European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46466-4_52"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Wojke, N., and Bewley, A. (2018, January 12\u201315). Deep Cosine Metric Learning for Person Re-identification. Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA.","DOI":"10.1109\/WACV.2018.00087"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Cruz, R., Garrote, L., Lopes, A., and Nunes, U.J. (2018, January 25\u201327). Modular software architecture for human-robot interaction applied to the InterBot mobile robot. Proceedings of the IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), Torres Vedras, Portugal.","DOI":"10.1109\/ICARSC.2018.8374154"}],"container-title":["Applied Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-3417\/12\/3\/1319\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:08:19Z","timestamp":1760134099000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-3417\/12\/3\/1319"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,26]]},"references-count":43,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["app12031319"],"URL":"https:\/\/doi.org\/10.3390\/app12031319","relation":{},"ISSN":["2076-3417"],"issn-type":[{"value":"2076-3417","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,26]]}}}