{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:45:15Z","timestamp":1760233515239,"version":"build-2065373602"},"reference-count":51,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,1,20]],"date-time":"2021-01-20T00:00:00Z","timestamp":1611100800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Special Funding of the \u2018Belt and Road\u2019 International Cooperation of Zhejiang Province","award":["2015C04005"],"award-info":[{"award-number":["2015C04005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper explored a pragmatic approach to research the real-time performance of a multiway concurrent multiobject tracking (MOT) system. At present, most research has focused on the tracking of single-image sequences, but in practical applications, multiway video streams need to be processed in parallel by MOT systems. There have been few studies on the real-time performance of multiway concurrent MOT systems. In this paper, we proposed a new MOT framework to solve multiway concurrency scenario based on a tracking-by-detection (TBD) model. The new framework mainly focuses on concurrency and real-time based on limited computing and storage resources, while considering the algorithm performance. For the former, three aspects were studied: (1) Expanded width and depth of tracking-by-detection model. In terms of width, the MOT system can support the process of multiway video sequence at the same time; in terms of depth, image collectors and bounding box collectors were introduced to support batch processing. (2) Considering the real-time performance and multiway concurrency ability, we proposed one kind of real-time MOT algorithm based on directly driven detection. (3) Optimization of system level\u2014we also utilized the inference optimization features of NVIDIA TensorRT to accelerate the deep neural network (DNN) in the tracking algorithm. To trade off the performance of the algorithm, a negative sample (false detection sample) filter was designed to ensure tracking accuracy. Meanwhile, the factors that affect the system real-time performance and concurrency were studied. The experiment results showed that our method has a good performance in processing multiple concurrent real-time video streams.<\/jats:p>","DOI":"10.3390\/s21030685","type":"journal-article","created":{"date-parts":[[2021,1,21]],"date-time":"2021-01-21T00:53:41Z","timestamp":1611190421000},"page":"685","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Real-Time Multiobject Tracking Based on Multiway Concurrency"],"prefix":"10.3390","volume":"21","author":[{"given":"Xuan","family":"Gong","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China"},{"name":"College of Science, Zhejiang University of Technology, Hangzhou 310023, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9745-8565","authenticated-orcid":false,"given":"Zichun","family":"Le","sequence":"additional","affiliation":[{"name":"College of Science, Zhejiang University of Technology, Hangzhou 310023, China"}]},{"given":"Yukun","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China"},{"name":"School of Artificial Intelligence, Zhejiang Post and Telecommunication College, Shaoxing 312366, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5837-4492","authenticated-orcid":false,"given":"Hui","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.patrec.2012.07.005","article-title":"Intelligent multi-camera video surveillance: A review","volume":"34","author":"Wang","year":"2013","journal-title":"Pattern Recognit. Lett."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Pfister, T., Charles, J., and Zisserman, A. (2015, January 7\u201313). Flowing convnets for human pose estimation in videos. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.222"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Choi, W., and Savarese, S. (2012, January 7\u201313). A unified framework for multi-target tracking and collective activity recognition. Proceedings of the 12th European Conference on Computer Vision (ECCV), Florence, Italy.","DOI":"10.1007\/978-3-642-33765-9_16"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1109\/TSMCC.2004.829274","article-title":"A survey on visual surveillance of object motion and behaviors","volume":"34","author":"Hu","year":"2004","journal-title":"IEEE Trans. Syst. Man Cybern. Part C Appl. Rev."},{"key":"ref_5","unstructured":"Luo, W., Xing, J., Milan, A., Zhang, X., Liu, W., Zhao, X., and Kim, T.K. (2014). Multiple object tracking: A literature review. arXiv."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Manzo, M., and Pellino, S. (2020). FastGCN + ARSRGemb: A novel framework for object recognition. arXiv.","DOI":"10.1117\/1.JEI.30.3.033011"},{"key":"ref_7","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 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA.","DOI":"10.1109\/ICIP.2016.7533003"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1115\/1.3662552","article-title":"A New Approach to Linear Filtering and Prediction Problems","volume":"82","author":"Kalman","year":"1960","journal-title":"ASME J. Basic Eng."},{"key":"ref_9","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_10","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 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China.","DOI":"10.1109\/ICIP.2017.8296962"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Yang, X., Yu, Z., Zheng, L., Yang, Y., and Kautz, J. (2019, January 15\u201320). Joint Discriminative and Generative Learning for Person Re-Identification. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00224"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Kim, C., Li, F., Ciptadi, A., and Rehg, J.M. (2015, January 7\u201313). Multiple Hypothesis Tracking Revisited. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.533"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Chu, Q., Ouyang, W., Li, H., Wang, X., Liu, B., and Yu, N. (2017, January 22\u201329). Online multi-object Tracking Using cnn-based Single Object Tracker with spatial-temporal Attention Mechanism. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.518"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zheng, L., Liu, Y., Li, Y., and Wang, S. (2019). Towards real-time multi-object tracking. arXiv.","DOI":"10.1007\/978-3-030-58621-8_7"},{"key":"ref_15","unstructured":"Milan, A., Leal-Taix\u2019e, L., Reid, I., Roth, S., and Schindler, K. (2016). Mot16: A benchmark for multi-object tracking. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Fang, K., Xiang, Y., Li, X., and Savarese, S. (2018, January 12\u201315). Recurrent Autoregressive Networks for Online Multi-Object Tracking. Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA.","DOI":"10.1109\/WACV.2018.00057"},{"key":"ref_17","unstructured":"Leal-Taix\u2019e, L., Milan, A., Reid, I., Roth, S., and Schindler, K. (2015). MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1109\/TITS.2019.2892413","article-title":"Online Multi-Object Tracking Using Joint Domain Information in Traffic Scenarios","volume":"21","author":"Tian","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"182828","DOI":"10.1109\/ACCESS.2020.3028770","article-title":"Online Multiple Object Tracking Using Rule Distillated Siamese Random Forest","volume":"8","author":"Lee","year":"2020","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Pang, B., Li, Y., Zhang, Y., Li, M., and Lu, C. (2020, January 13\u201319). TubeTK: Adopting Tubes to Track Multi-Object in a One-Step Training Model. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00634"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"6694","DOI":"10.1109\/TIP.2020.2993073","article-title":"Long-term tracking with deep tracklet association","volume":"29","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Milioto, A., and Stachniss, C. (2019, January 20\u201324). Bonnet: An open-source Training and Deployment Framework for Semantic Segmentation in Robotics using CNNs. Proceedings of the 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada.","DOI":"10.1109\/ICRA.2019.8793510"},{"key":"ref_23","unstructured":"Jain, P., Mo, X., Jain, A., Subbaraj, H., Durrani, R.S., Tumanov, A., and Stoica, I. (2018). Dynamic space-time Scheduling for GPU Inference. arXiv."},{"key":"ref_24","unstructured":"Alyamkin, S., Ardi, M., Brighton, A., Berg, A.C., Chen, Y., Cheng, H.P., and Gauen, K. (2018). 2018 Low-Power Image Recognition Challenge. arXiv."},{"key":"ref_25","unstructured":"Singhani, A. (2019). Real-time Freespace Segmentation on Autonomous Robots for Detection of diction and drop-offs. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Womg, A., Shafiee, M.J., Li, F., and Chwyl, B. (2018, January 8\u201310). Tiny SSD: A tiny single-shot detection deep convolutional neural network for real-time embedded object detection. Proceedings of the 2018 15th Conference on Computer and Robot Vision (CRV), Toronto, ON, Canada.","DOI":"10.1109\/CRV.2018.00023"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Lan, W., Dang, J., Wang, Y., and Wang, S. (2018, January 5\u20138). Pedestrian detection based on yolo network model. Proceedings of the 2018 IEEE International Conference on Mechatronics and Automation (ICMA), Changchun, China.","DOI":"10.1109\/ICMA.2018.8484698"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Mane, S., and Mangale, S. (2018, January 14\u201315). Moving object detection and tracking using convolutional neural networks. Proceedings of the 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India.","DOI":"10.1109\/ICCONS.2018.8662921"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Huang, Y., and Wang, L. (2016, January 16\u201318). What makes for good multiple object trackers?. Proceedings of the 2016 IEEE International Conference on Digital Signal Processing (DSP), Beijing, China.","DOI":"10.1109\/ICDSP.2016.7868601"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Abbas, S.M., and Singh, S.N. (2018, January 9\u201310). Region-based object detection and classification using faster R-CNN. Proceedings of the 2018 4th International Conference on Computational Intelligence & Communication Technology (CICT), Ghaziabad, India.","DOI":"10.1109\/CIACT.2018.8480413"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"\u00d6zer, C., G\u00fcrkan, F., and G\u00fcnsel, B. (2018, January 2\u20135). Object tracking by deep object detectors and particle filtering. Proceedings of the 2018 26th Signal Processing and Communications Applications Conference (SIU), Izmir, Turkey.","DOI":"10.1109\/SIU.2018.8404622"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Liu, Y., Wang, P., and Wang, H. (2018, January 10\u201312). Target tracking algorithm based on deep learning and multi-video monitoring. Proceedings of the 2018 5th International Conference on Systems and Informatics (ICSAI), Nanjing, China.","DOI":"10.1109\/ICSAI.2018.8599349"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhao, X., Li, W., Zhang, Y., Gulliver, T.A., Chang, S., and Feng, Z. (2016, January 18\u201321). A faster RCNN-based pedestrian detection system. Proceedings of the 2016 IEEE 84th Vehicular Technology Conference (VTC-Fall), Montreal, QC, Canada.","DOI":"10.1109\/VTCFall.2016.7880852"},{"key":"ref_34","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1109\/78.978374","article-title":"A tutorial on particle filters for online nonlinear\/non-Gaussian Bayesian tracking","volume":"50","author":"Arulampalam","year":"2002","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_36","first-page":"73","article-title":"Survey of online multi-object video tracking algorithms","volume":"37","author":"Li","year":"2018","journal-title":"Comput. Technol. Autom."},{"key":"ref_37","first-page":"164","article-title":"Summary of data association methods in Multi-target tracking","volume":"6","author":"Yang","year":"2016","journal-title":"Sci. Technol. Vis."},{"key":"ref_38","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_39","unstructured":"Ran, N., Kong, L., Wang, Y., and Liu, Q. (2019, January 8\u201311). A robust multi-athlete tracking algorithm by exploiting discriminant features and long-term dependencies. Proceedings of the 25th International Conference on MultiMedia Modeling (MMM 2019), Thessaloniki, Greece."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3093","DOI":"10.1109\/ACCESS.2018.2889187","article-title":"Recurrent metric networks and batch multiple hypothesis for multi-object tracking","volume":"7","author":"Chen","year":"2019","journal-title":"IEEE Access"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"27923","DOI":"10.1109\/ACCESS.2019.2901520","article-title":"Online multi-object tracking based on feature representation and bayesian filtering within a deep learning architecture","volume":"7","author":"Xiang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"559","DOI":"10.3390\/s19030559","article-title":"Data association for multi-object tracking via deep neural networks","volume":"19","author":"Kwangjin","year":"2019","journal-title":"Sensors"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Bolme, D.S., Beveridge, J.R., Draper, B.A., and Lui, Y.M. (2010, January 13\u201318). Visual Object Tracking using Adaptive Correlation Filters. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5539960"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Henriques, J.F., Caseiro, R., Martins, P., and Batista, J. (2012, January 7\u201313). Exploiting the circulant structure of tracking-by-detection with kernels. Proceedings of the 2012 European Conference on Computer Vision (ECCV), Florence, Italy.","DOI":"10.1007\/978-3-642-33765-9_50"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1109\/TPAMI.2014.2345390","article-title":"High-speed Tracking with Kernelized Correlation Filters","volume":"37","author":"Henriques","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Danelljan, M., H\u00e4ger, G., Khan, F., and Felsberg, M. (2014, January 1\u20135). Accurate Scale Estimation for Robust Visual Tracking. Proceedings of the 2014 British Machine Vision Conference (BMVC), Nottingham, UK.","DOI":"10.5244\/C.28.65"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1561","DOI":"10.1109\/TPAMI.2016.2609928","article-title":"Discriminative Scale Space Tracking","volume":"39","author":"Danelljan","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Bernardin, K., and Stiefelhagen, R. (2008). Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. EURASIP J. Image Video Process., 1\u201310.","DOI":"10.1155\/2008\/246309"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Yu, F., Li, W., Li, Q., Liu, Y., Shi, X., and Yan, J. (2016, January 8\u201316). POI: Multiple Object Tracking with High Performance Detection and Appearance Feature. Proceedings of the 2016 European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-48881-3_3"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Bergmann, P., Meinhardt, T., and Leal-Taix\u2019e, L. (November, January 27). Tracking without bells and whistles. Proceedings of the 2019 IEEE International Conference on Computer Vision (ICCV), Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00103"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Peng, J., Wang, T., Lin, W., Wang, J., See, J., Wen, S., and Ding, E. (2020). TPM: Multiple Object Tracking with Tracklet-Plane Matching. Pattern Recognit., 107480.","DOI":"10.1016\/j.patcog.2020.107480"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/3\/685\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:13:00Z","timestamp":1760159580000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/3\/685"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,20]]},"references-count":51,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["s21030685"],"URL":"https:\/\/doi.org\/10.3390\/s21030685","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2021,1,20]]}}}