{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T00:56:21Z","timestamp":1769561781958,"version":"3.49.0"},"reference-count":38,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,6,2]],"date-time":"2023-06-02T00:00:00Z","timestamp":1685664000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Multiple-Object Tracking (MOT) has become more popular because of its commercial and academic potential. Though various techniques were devised for managing this issue, it becomes a challenge because of factors such as severe object occlusions and abrupt appearance changes. Tracking presents the optimal outcomes whenever the object moves uniformly without occlusion and in the same direction. However, this is generally not a real scenario, particularly in complicated scenes such as dance events or sporting where a greater number of players are tracked, moving quickly, varying their speed and direction, along with distance and position from the camera and activity they are executing. In dynamic scenes, MOT remains the main difficulty due to the symmetrical shape, structure, and size of the objects. Therefore, this study develops a new reptile search optimization algorithm with deep learning-based multiple object detection and tracking (RSOADL\u2013MODT) techniques. The presented RSOADL\u2013MODT model intends to recognize and track the objects that exist with position estimation, tracking, and action recognition. It follows a series of processes, namely object detection, object classification, and object tracking. At the initial stage, the presented RSOADL\u2013MODT technique applies a path-augmented RetinaNet-based (PA\u2013RetinaNet) object detection module, which improves the feature extraction process. To improvise the network potentiality of the PA\u2013RetinaNet method, the RSOA is utilized as a hyperparameter optimizer. Finally, the quasi-recurrent neural network (QRNN) classifier is exploited for classification procedures. A wide-ranging experimental validation process takes place on DanceTrack and MOT17 datasets for examining the effectual object detection outcomes of the RSOADL\u2013MODT algorithm. The simulation values confirmed the enhancements of the RSOADL\u2013MODT method over other DL approaches.<\/jats:p>","DOI":"10.3390\/sym15061194","type":"journal-article","created":{"date-parts":[[2023,6,2]],"date-time":"2023-06-02T10:08:41Z","timestamp":1685700521000},"page":"1194","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Multi-Object Detection and Tracking Using Reptile Search Optimization Algorithm with Deep Learning"],"prefix":"10.3390","volume":"15","author":[{"given":"Ramachandran","family":"Alagarsamy","sequence":"first","affiliation":[{"name":"Department of Electronics and Communication Engineering, SSM Institute of Engineering and Technology, Dindigul 624002, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9317-2917","authenticated-orcid":false,"given":"Dhamodaran","family":"Muneeswaran","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, M.Kumarasamy College of Engineering, Karur 639113, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.neucom.2022.11.094","article-title":"Center-point-pair detection and context-aware re-identification for end-to-end multi-object tracking","volume":"524","author":"Zhang","year":"2023","journal-title":"Neurocomputing"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Guo, S., Wang, S., Yang, Z., Wang, L., Zhang, H., Guo, P., Gao, Y., and Guo, J. (2022). A Review of Deep Learning-Based Visual Multi-Object Tracking Algorithms for Autonomous Driving. Appl. Sci., 12.","DOI":"10.3390\/app122110741"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Pearce, A., Zhang, J.A., Xu, R., and Wu, K. (2023). Multi-Object tracking with mmWave Radar: A Review. Electronics, 12.","DOI":"10.3390\/electronics12020308"},{"key":"ref_4","unstructured":"Cao, J., Weng, X., Khirodkar, R., Pang, J., and Kitani, K. (2022). Observation-centric sort: Rethinking sort for robust multi-object tracking. arXiv."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"6400","DOI":"10.1007\/s10489-021-02293-7","article-title":"Deep learning in multi-object detection and tracking: State of the art","volume":"51","author":"Pal","year":"2021","journal-title":"Appl. Intell."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"18171","DOI":"10.1007\/s00521-022-07456-2","article-title":"Similarity based person re-identification for multi-object tracking using deep Siamese network","volume":"34","author":"Suljagic","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Valverde, F.R., Hurtado, J.V., and Valada, A. (2021, January 20\u201325). There is more than meets the eye: Self-supervised multi-object detection and tracking with sound by distilling multimodal knowledge. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01144"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Sun, P., Jiang, Y., Yu, D., Weng, F., Yuan, Z., Luo, P., Liu, W., and Wang, X. (2022, January 23\u201327). Bytetrack: Multi-object tracking by associating every detection box. Proceedings of the Computer Vision\u2013ECCV 2022: 17th European Conference, Tel Aviv, Israel. Part XXII.","DOI":"10.1007\/978-3-031-20047-2_1"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5668","DOI":"10.1109\/JSEN.2020.3041615","article-title":"Multi-object detection and tracking, based on DNN, for autonomous vehicles: A review","volume":"21","author":"Ravindran","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1109\/TIP.2022.3227814","article-title":"A Closer Look at the Joint Training of Object Detection and Re-Identification in Multi-Object Tracking","volume":"32","author":"Liang","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"8260","DOI":"10.1109\/LRA.2022.3187264","article-title":"DeepFusionMOT: A 3D Multi-Object Tracking Framework Based on Camera-LiDAR Fusion with Deep Association","volume":"7","author":"Wang","year":"2022","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wang, Y., Kitani, K., and Weng, X. (June, January 30). Joint object detection and multi-object tracking with graph neural networks. Proceedings of the 2021 IEEE International Conference on Robotics and Automation (ICRA) 2021, Xi\u2019an, China.","DOI":"10.1109\/ICRA48506.2021.9561110"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1007\/978-981-16-5987-4_73","article-title":"Real-time multi-object tracking of pedestrians in a video using convolution neural network and Deep SORT","volume":"Volume 1","author":"Praveenkumar","year":"2022","journal-title":"Proceedings of the ICT Systems and Sustainability: Proceedings of ICT4SD 2021"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1109\/TIV.2022.3158419","article-title":"3D multi-object tracking with adaptive cubature Kalman filter for autonomous driving","volume":"8","author":"Guo","year":"2022","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"13401","DOI":"10.1007\/s11042-022-13717-y","article-title":"Maximum entropy scaled super pixels segmentation for multi-object detection and scene recognition via deep belief network","volume":"82","author":"Rafique","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Lusardi, C., Taufique, A.M.N., and Savakis, A. (2021, January 11\u201317). Robust multi-object tracking using re-identification features and graph convolutional networks. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCVW54120.2021.00433"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Jiang, T., Zhang, Q., Yuan, J., Wang, C., and Li, C. (2022). Multi-Type Object Tracking Based on Residual Neural Network Model. Symmetry, 14.","DOI":"10.3390\/sym14081689"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zhang, Z., Zhang, N., and Zeng, D. (2021). Attention Modulated Multiple Object Tracking with Motion Enhancement and Dual Correlation. Symmetry, 13.","DOI":"10.3390\/sym13020266"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhou, X., Koltun, V., and Kr\u00e4henb\u00fchl, P. (2020, January 23\u201328). Tracking objects as points. Proceedings of the Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK.","DOI":"10.1007\/978-3-030-58548-8_28"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3069","DOI":"10.1007\/s11263-021-01513-4","article-title":"Fairmot: On the fairness of detection and re-identification in multiple object tracking","volume":"129","author":"Zhang","year":"2021","journal-title":"IJCV"},{"key":"ref_21","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, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01217"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhou, X., Yin, T., Koltun, V., and Krahenbuhl, P. (2022, January 19\u201320). Global tracking transformers. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00857"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zeng, F., Dong, B., Zhang, Y., Wang, T., Zhang, X., and Wei, Y. (2022, January 23\u201327). Motr: End-to-end multiple object tracking with transformer. Proceedings of the European Conference on Computer Vision (ECCV), Tel Aviv, Israel.","DOI":"10.1007\/978-3-031-19812-0_38"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Pang, J., Qiu, L., Li, X., Chen, H., Li, Q., Darrell, T., and Yu, F. (2021, January 20\u201325). Quasi-dense similarity learning for multiple object tracking. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00023"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1016\/j.asoc.2018.05.023","article-title":"Soft Computing based object detection and tracking approaches: State-of-the-Art survey","volume":"70","author":"Kaushal","year":"2018","journal-title":"Appl. Soft Comput."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Castro, E.C.d., Salles, E.O.T., and Ciarelli, P.M. (2021). A New Approach to Enhanced Swarm Intelligence Applied to Video Target Tracking. Sensors, 21.","DOI":"10.3390\/s21051903"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1705","DOI":"10.1016\/j.ijleo.2015.05.028","article-title":"Firefly algorithm (FA) based particle filter method for visual tracking","volume":"126","author":"Gao","year":"2015","journal-title":"Optik"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"6315","DOI":"10.1016\/j.eswa.2014.03.012","article-title":"Intelligent video target tracking using an evolutionary particle filter based upon improved cuckoo search","volume":"41","author":"Walia","year":"2014","journal-title":"Expert Syst. Appl."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wang, N., Shi, J., Yeung, D.-Y., and Jia, J. (2015, January 7\u201313). Understanding and diagnosing visual tracking systems. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.355"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Tan, G., Guo, Z., and Xiao, Y. (2019, January 17\u201319). PA-RetinaNet: Path augmented RetinaNet for dense object detection. Proceedings of the Artificial Neural Networks and Machine Learning\u2013ICANN 2019: Deep Learning, Munich, Germany.","DOI":"10.1007\/978-3-030-30484-3_12"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Khan, M.K., Zafar, M.H., Rashid, S., Mansoor, M., Moosavi, S.K.R., and Sanfilippo, F. (2023). Improved Reptile Search Optimization Algorithm: Application on Regression and Classification Problems. Appl. Sci., 13.","DOI":"10.3390\/app13020945"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"012040","DOI":"10.1088\/1755-1315\/696\/1\/012040","article-title":"A parallel electrical optimized load forecasting method based on quasi-recurrent neural network","volume":"696","author":"Yang","year":"2021","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Sun, P., Cao, J., Jiang, Y., Yuan, Z., Bai, S., Kitani, K., and Luo, P. (2022, January 18\u201324). Dancetrack: Multi-object tracking in uniform appearance and diverse motion. Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.02032"},{"key":"ref_34","unstructured":"Milan, A., Leal-Taix\u00e9, L., Reid, I., Roth, S., and Schindler, K. (2016). MOT16: A benchmark for multi-object tracking. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"246309","DOI":"10.1155\/2008\/246309","article-title":"Evaluating multiple object tracking performance: The clear mot metrics","volume":"2008","author":"Bernardin","year":"2008","journal-title":"EURASIP J. Image Video Process."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ristani, E., Solera, F., Zou, R., Cucchiara, R., and Tomasi, C. (2016, January 11\u201314). Performance measures and a data set for multi-target, multi-camera tracking. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-48881-3_2"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1007\/s11263-020-01375-2","article-title":"Hota: A higher order metric for evaluating multi-object tracking","volume":"129","author":"Luiten","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Braso, G., and Leal-Taixe, L. (2020, January 13\u201319). Learning a neural solver for multiple object tracking. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00628"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/15\/6\/1194\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:48:10Z","timestamp":1760125690000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/15\/6\/1194"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,2]]},"references-count":38,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["sym15061194"],"URL":"https:\/\/doi.org\/10.3390\/sym15061194","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,2]]}}}