{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T04:27:00Z","timestamp":1787113620140,"version":"build-2736575974"},"reference-count":38,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T00:00:00Z","timestamp":1786924800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"crossref","award":["RS-2024-00412141"],"award-info":[{"award-number":["RS-2024-00412141"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"crossref"}]},{"award":["RS-2024-00412141"],"award-info":[{"award-number":["RS-2024-00412141"]}],"id":[{"id":"https:\/\/ror.org\/013aysd81","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Regional Innovation System & Education","award":["2026-RISE-11-003-03"],"award-info":[{"award-number":["2026-RISE-11-003-03"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Top-loading smart vending cabinets require precise transaction-level product detection under strict hardware and deployment constraints. In this paper, \u201csensorless\u201d refers specifically to the absence of auxiliary product-level sensing hardware, such as RFID tags, weight sensors, shelf load cells, or product-slot instrumentation; the system still uses two camera sensors. Conventional snapshot-difference methods compare only a small number of frames at the beginning and end of a transaction and therefore cannot explicitly represent intermediate product motion, such as pickup, return, inspection, occlusion, and shelf resettling. This paper proposes ZAB-Fusion, a dual-camera edge sensing framework with zone-aware multi-object tracking for sensorless smart vending cabinets. The framework combines a YOLO11-seg and RT-DETR detection ensemble with ByteTrack temporal association, projects product tracks into a three-zone vertical cabinet model, interprets compressed zone sequences using a finite-state event classifier, and integrates camera-specific event streams through an evidence-gated cross-camera fusion rule. The proposed method was evaluated on 220 in-service vending transactions containing 227 ground-truth TAKEN events and 87 RETURNED events across seven product classes. Compared with the snapshot-difference baseline, ZAB-Fusion improved recall from 0.665 to 0.925 and F1-score from 0.780 to 0.944, while maintaining a high precision of 0.963. At the transaction level, exact receipt accuracy increased from 0.645 to 0.900. Runtime analysis on an Intel N100 CPU-only edge device showed an average processing latency of 562 ms per transaction under the selected-frame inference protocol. The zone classification, finite-state event interpretation, and evidence-gated cross-camera fusion stages required only 3 ms in total. These results demonstrate that explicit motion semantics and auditable cross-camera evidence gating can improve sensorless retail transaction level recognition in sensorless smart vending cabinets without adding auxiliary product-level sensing hardware.<\/jats:p>","DOI":"10.3390\/s26165213","type":"journal-article","created":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T16:35:08Z","timestamp":1786984508000},"page":"5213","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Dual-Camera Edge Sensing Framework with Zone-Aware Multi-Object Tracking for Sensorless Smart Vending Cabinets"],"prefix":"10.3390","volume":"26","author":[{"given":"Abror Shavkatovich","family":"Buriboev","sequence":"first","affiliation":[{"name":"Department of Artificial Intelligence, Gachon University, Seongnam-si 13120, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0917-945X","authenticated-orcid":false,"given":"Farkhat","family":"Rajabov","sequence":"additional","affiliation":[{"name":"Department of Computer Systems, Tashkent University of Information Technologies, Tashkent 100084, Uzbekistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shavkat","family":"Buriboev","sequence":"additional","affiliation":[{"name":"Department of Construction Engineering, Samarkand State Technical University, Samarkand 140100, Uzbekistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7189-2818","authenticated-orcid":false,"given":"Rustem","family":"Allanyazov","sequence":"additional","affiliation":[{"name":"Department of Computer Systems, Tashkent University of Information Technologies, Tashkent 100084, Uzbekistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giyosjon","family":"Sharipov","sequence":"additional","affiliation":[{"name":"Department of Metrology and Standardization, Namangan State Technical University, Namangan 160100, Uzbekistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abbos","family":"Abduvaytov","sequence":"additional","affiliation":[{"name":"Department of Information Technologies, Samarkand Institute of Economics and Service, Samarkand 140100, Uzbekistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aziza","family":"Akhmedova","sequence":"additional","affiliation":[{"name":"Department of Exact Sciences, Kimyo International University in Tashkent, Tashkent 100121, Uzbekistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruzimboy","family":"Sobirov","sequence":"additional","affiliation":[{"name":"Department of Digital Technologies and Artificial Intelligence, Tashkent Institute of Irrigation and Agricultural Mechanization Engineers, National Research University, Tashkent 100000, Uzbekistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Su-Mi","family":"Shin","sequence":"additional","affiliation":[{"name":"Data Service Center, Korea Institute of Science and Technology Information, Seoul 02456, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2778-2562","authenticated-orcid":false,"given":"Cheolwon","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, AI Convergence Research Center, Konkuk University, Chungju 27478, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heung Seok","family":"Jeon","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, AI Convergence Research Center, Konkuk University, Chungju 27478, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,17]]},"reference":[{"key":"ref_1","first-page":"427","article-title":"Systematic Review of Cashierless Stores (Just Walk Out Stores): Revolutionizing the Retail","volume":"18","year":"2023","journal-title":"Manag. Mark. Chall. Knowl. Soc."},{"key":"ref_2","first-page":"38","article-title":"Cashierless Stores\u2014The New Way to the Customer?","volume":"37","author":"Lienhard","year":"2020","journal-title":"Mark. Rev. St. Gall."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/MCE.2021.3060722","article-title":"A Design of Smart Unmanned Vending Machine for New Retail Based on Binocular Camera and Machine Vision","volume":"11","author":"Liu","year":"2022","journal-title":"IEEE Consum. Electron. Mag."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Xu, J., Li, Y., Wang, J., Li, X., and Zhang, Y. (2023). Research on Product Detection and Recognition Methods for Intelligent Vending Machines. Front. Neurosci., 17.","DOI":"10.3389\/fnins.2023.1288908"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Buriboev, A.S., Abduvaitov, A., and Jeon, H.S. (2025). Integrating Color and Contour Analysis with Deep Learning for Robust Fire and Smoke Detection. Sensors, 25.","DOI":"10.3390\/s25072044"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"21038","DOI":"10.1109\/ACCESS.2024.3361675","article-title":"BP-YOLO: A Real-Time Product Detection and Shopping Behaviors Recognition Model for Intelligent Unmanned Vending Machine","volume":"12","author":"Li","year":"2024","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1738","DOI":"10.1109\/JPROC.2019.2918951","article-title":"Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing","volume":"107","author":"Zhou","year":"2019","journal-title":"Proc. IEEE"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Buriboev, A., and Muminov, A. (2022). Computer State Evaluation Using Adaptive Neuro-Fuzzy Inference Systems. Sensors, 22.","DOI":"10.3390\/s22239502"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.iotcps.2023.02.004","article-title":"Edge AI: A Survey","volume":"3","author":"Singh","year":"2023","journal-title":"Internet Things Cyber-Phys. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"7457","DOI":"10.1109\/JIOT.2020.2984887","article-title":"Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence","volume":"7","author":"Deng","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Merenda, M., Porcaro, C., and Iero, D. (2020). Edge Machine Learning for AI-Enabled IoT Devices: A Review. Sensors, 20.","DOI":"10.3390\/s20092533"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1109\/COMST.2020.2970550","article-title":"Convergence of Edge Computing and Deep Learning: A Comprehensive Survey","volume":"22","author":"Wang","year":"2020","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_13","unstructured":"Wei, X.-S., Cui, Q., Yang, L., Wang, P., and Liu, L. (2019). RPC: A Large-Scale Retail Product Checkout Dataset. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Goldman, E., Herzig, R., Eisenschtat, A., Goldberger, J., and Hassner, T. (2019, January 15\u201320). Precise Detection in Densely Packed Scenes. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00537"},{"key":"ref_15","unstructured":"Bai, Y., Chen, Y., Yu, W., Wang, L., and Zhang, W. (2020). Products-10K: A Large-Scale Product Recognition Dataset. arXiv."},{"key":"ref_16","unstructured":"Jund, P., Abdo, N., Eitel, A., and Burgard, W. (2016). The Freiburg Groceries Dataset. arXiv."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"George, M., and Floerkemeier, C. (2014, January 6\u20137). Recognizing Products: A Per-Exemplar Multi-Label Image Classification Approach. Proceedings of the European Conference on Computer Vision Workshops (ECCV Workshops), Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10605-2_29"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Merler, M., Galleguillos, C., and Belongie, S. (2007, January 17\u201322). Recognizing Groceries in Situ Using In Vitro Training Data. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops), Minneapolis, MN, USA.","DOI":"10.1109\/CVPR.2007.383486"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Tojiyev, M., Himmatov, I., and Chiniyev, O. (2026, January 23\u201327). Intelligent Adaptive Algorithm for Boundary Detection of Dynamically Changing Regions in Video Sequences. Proceedings of the 2026 International Russian Smart Industry Conference (SmartIndustryCon), Sochi, Russia.","DOI":"10.1109\/SmartIndustryCon68821.2026.11492982"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal Loss for Dense Object Detection. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Tagmatova, Z., Umirzakova, S., Kutlimuratov, A., Abdusalomov, A., and Im Cho, Y. (2025). A Hyper-Attentive Multimodal Transformer for Real-Time and Robust Facial Expression Recognition. Appl. Sci., 15.","DOI":"10.3390\/app15137100"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"329","DOI":"10.11591\/ijai.v13.i1.pp329-336","article-title":"Choosing allowability boundaries for describing objects in subject areas","volume":"13","author":"Lolaev","year":"2024","journal-title":"IAES Int. J. Artif. Intell. (IJ-AI)"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Lv, W., Xu, S., Wei, J., Wang, G., Dang, Q., Liu, Y., and Chen, J. (2024, January 17\u201321). DETRs Beat YOLOs on Real-Time Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.01605"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"8875910","DOI":"10.1155\/2020\/8875910","article-title":"Deep Learning for Retail Product Recognition: Challenges and Techniques","volume":"2020","author":"Wei","year":"2020","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"19838","DOI":"10.1038\/s41598-024-71089-9","article-title":"Smart Customer Service in Unmanned Retail Store enhanced by large language model","volume":"14","author":"Wang","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_27","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":"J. Basic Eng."},{"key":"ref_28","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_29","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_30","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_31","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":"Int. J. Comput. Vis."},{"key":"ref_32","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 European Conference on Computer Vision (ECCV), Tel Aviv, Israel.","DOI":"10.1007\/978-3-031-20047-2_1"},{"key":"ref_33","unstructured":"Aharon, N., Orfaig, R., and Bobrovsky, B.-Z. (2022). BoT-SORT: Robust Associations Multi-Pedestrian Tracking. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Cao, J., Weng, X., Khirodkar, R., Pang, J., and Kitani, K. (2023, January 18\u201322). Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00934"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Maggiolino, G., Ahmad, A., Cao, J., and Kitani, K. (2023, January 8\u201311). Deep OC-SORT: Multi-Pedestrian Tracking by Adaptive Re-Identification. Proceedings of the IEEE International Conference on Image Processing (ICIP), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICIP49359.2023.10222576"},{"key":"ref_36","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_37","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_38","unstructured":"Dendorfer, P., Rezatofighi, H., Milan, A., Shi, J., Cremers, D., Reid, I., Roth, S., Schindler, K., and Leal-Taix\u00e9, L. (2020). MOT20: A Benchmark for Multi-Object Tracking in Crowded Scenes. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/16\/5213\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T04:12:01Z","timestamp":1787112721000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/16\/5213"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,17]]},"references-count":38,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["s26165213"],"URL":"https:\/\/doi.org\/10.3390\/s26165213","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,17]]}}}