{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T01:50:18Z","timestamp":1768528218149,"version":"3.49.0"},"reference-count":83,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2019,9,13]],"date-time":"2019-09-13T00:00:00Z","timestamp":1568332800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"scientific research fund project of Xijing University","award":["No. XJ170204"],"award-info":[{"award-number":["No. XJ170204"]}]},{"name":"Shaanxi Key Laboratory of Integrated and Intelligent Navigation open fund","award":["No. SKLIIN-20180211"],"award-info":[{"award-number":["No. SKLIIN-20180211"]}]},{"name":"the natural science foundation research project of Shaanxi province, China","award":["2018JM6098"],"award-info":[{"award-number":["2018JM6098"]}]},{"name":"the research foundation for talented scholars of Xijing University","award":["XJ17B06"],"award-info":[{"award-number":["XJ17B06"]}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61473237, 61801382"],"award-info":[{"award-number":["61473237, 61801382"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018537","name":"National Science and Technology Major Project of the Ministry of Science and Technology of China","doi-asserted-by":"publisher","award":["ZX201703001012-005"],"award-info":[{"award-number":["ZX201703001012-005"]}],"id":[{"id":"10.13039\/501100018537","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the China Postdoctoral Science Foundation","award":["2018M633679"],"award-info":[{"award-number":["2018M633679"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>In the context of Industry 4.0, the most popular way to identify and track objects is to add tags, and currently most companies still use cheap quick response (QR) tags, which can be positioned by computer vision (CV) technology. In CV, instance segmentation (IS) can detect the position of tags while also segmenting each instance. Currently, the mask region-based convolutional neural network (Mask R-CNN) method is used to realize IS, but the completeness of the instance mask cannot be guaranteed. Furthermore, due to the rich texture of QR tags, low-quality images can lower intersection-over-union (IoU) significantly, disabling it from accurately measuring the completeness of the instance mask. In order to optimize the IoU of the instance mask, a QR tag IS method named the mask UNet region-based convolutional neural network (MU R-CNN) is proposed. We utilize the UNet branch to reduce the impact of low image quality on IoU through texture segmentation. The UNet branch does not depend on the features of the Mask R-CNN branch so its training process can be carried out independently. The pre-trained optimal UNet model can ensure that the loss of MU R-CNN is accurate from the beginning of the end-to-end training. Experimental results show that the proposed MU R-CNN is applicable to both high- and low-quality images, and thus more suitable for Industry 4.0.<\/jats:p>","DOI":"10.3390\/fi11090197","type":"journal-article","created":{"date-parts":[[2019,9,13]],"date-time":"2019-09-13T10:32:41Z","timestamp":1568370761000},"page":"197","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["MU R-CNN: A Two-Dimensional Code Instance Segmentation Network Based on Deep Learning"],"prefix":"10.3390","volume":"11","author":[{"given":"Baoxi","family":"Yuan","sequence":"first","affiliation":[{"name":"School of Information Engineering, Xijing University, Xi\u2019an 710123, China"},{"name":"Shaanxi Key Laboratory of Integrated and Intelligent Navigation, Xi\u2019an 710068, China"},{"name":"Beijing Jiurui Technology co., LTD, Beijing 100107, China"}]},{"given":"Yang","family":"Li","sequence":"additional","affiliation":[{"name":"Shaanxi Key Laboratory of Integrated and Intelligent Navigation, Xi\u2019an 710068, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8968-5178","authenticated-orcid":false,"given":"Fan","family":"Jiang","sequence":"additional","affiliation":[{"name":"Shaanxi Key Laboratory of Integrated and Intelligent Navigation, Xi\u2019an 710068, China"},{"name":"Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710121, China"}]},{"given":"Xiaojie","family":"Xu","sequence":"additional","affiliation":[{"name":"Shaanxi Key Laboratory of Integrated and Intelligent Navigation, Xi\u2019an 710068, China"}]},{"given":"Yingxia","family":"Guo","sequence":"additional","affiliation":[{"name":"Dongfanghong Middle School, Anding District, Dingxi City 743000, China"}]},{"given":"Jianhua","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Xijing University, Xi\u2019an 710123, China"}]},{"given":"Deyue","family":"Zhang","sequence":"additional","affiliation":[{"name":"Unit 95949 of CPLA, HeBei 061736, China"}]},{"given":"Jianxin","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Xijing University, Xi\u2019an 710123, China"},{"name":"Shaanxi Key Laboratory of Integrated and Intelligent Navigation, Xi\u2019an 710068, China"}]},{"given":"Xiaoli","family":"Shen","sequence":"additional","affiliation":[{"name":"Xi\u2019an Haitang Vocational College, Xi\u2019an 710038, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Uddin, M.T., and Uddiny, M.A. (2015, January 21\u201323). Human activity recognition from wearable sensors using extremely randomized trees. Proceedings of the International Conference on Electrical Engineering and Information Communication Technology, Dhaka, Bangladesh.","DOI":"10.1109\/ICEEICT.2015.7307384"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Jalal, A., Quaid, M.A.K., and Sidduqi, M.A. (2019, January 8\u201312). A Triaxial acceleration-based human motion detection for ambient smart home system. Proceedings of the IEEE International Conference on Applied Sciences and Technology, Islamabad, Pakistan.","DOI":"10.1109\/IBCAST.2019.8667183"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ahmed, A., Jalal, A., and Rafique, A.A. (2019, January 23\u201326). Salient Segmentation based Object Detection and Recognition using Hybrid Genetic Transform. Proceedings of the IEEE ICAEM Conference, Singapore.","DOI":"10.1109\/ICAEM.2019.8853834"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ahad, A.R., Kobashi, S., and Tavares, J.M.R.S. (2018). Advancements of image processing and vision in healthcare. J. Healthc. Eng., 2018.","DOI":"10.1155\/2018\/8458024"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Jalal, A., Nadeem, A., and Bobasu, S. (2019, January 6\u20137). Human body parts estimation and detection for physical sports movements. Proceedings of the IEEE International Conference on Communication, Computing and Digital Systems, Islamabad, Pakistan.","DOI":"10.1109\/C-CODE.2019.8680993"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Jalal, A., and Mahmood, M. (2019). Students\u2019 Behavior Mining in E-learning Environment Using Cognitive Processes with Information Technologies. Education and Information Technologies, Springer.","DOI":"10.1007\/s10639-019-09892-5"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Jalal, A. (2007, January 12\u201313). Security architecture for third generation (3G) using GMHS cellular network. Proceedings of the IEEE Conference on Emerging Technologies, Islamabad, Pakistan.","DOI":"10.1109\/ICET.2007.4516319"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Chen, I.K., Chi, C., Hsu, S., and Chen, L. (2014, January 10\u201313). A real-time system for object detection and location reminding with RGB-D camera. Proceedings of the 2014 IEEE International Conference on Consumer Electronics, Las Vegas, NV, USA.","DOI":"10.1109\/ICCE.2014.6776063"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Jalal, A., Mahmood, M., and Hasan, A.S. (2019, January 8\u201312). Multi-features descriptors for human activity tracking and recognition in Indoor-outdoor environments. Proceedings of the IEEE International Conference on Applied Sciences and Technology, Islamabad, Pakistan.","DOI":"10.1109\/IBCAST.2019.8667145"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Leila, M., Fonseca, G., Namikawa, L.M., and Castejon, E.F. (2009, January 11\u201314). Digital image processing in remote sensing. Proceedings of the Conference on Computer Graphics and Image Processing, Rio de Janeiro, Brazil.","DOI":"10.1109\/SIBGRAPI-Tutorials.2009.13"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Jalal, A., Kim, Y., and Kim, D. (2014, January 11\u201313). Ridge body parts features for human pose estimation and recognition from RGB-D video data. Proceedings of the IEEE International Conference on Computing, Communication and Networking Technologies, Hefei, China.","DOI":"10.1109\/ICCCNT.2014.6963015"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Jalal, A., Quaid, M.A.K., and Hasan, A.S. (2018, January 17\u201319). Wearable Sensor-Based Human Behavior Understanding and Recognition in Daily Life for Smart Environments. Proceedings of the IEEE Conference on International Conference on Frontiers of Information Technology, Islamabad, Pakistan.","DOI":"10.1109\/FIT.2018.00026"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Mahmood, M., Jalal, A., and Sidduqi, M.A. (2018, January 17\u201319). Robust spatio-temporal features for human interaction recognition via artificial neural network. Proceedings of the IEEE Conference on International Conference on Frontiers of Information Technology, Islamabad, Pakistan.","DOI":"10.1109\/FIT.2018.00045"},{"key":"ref_14","unstructured":"Prochdxka, A., Kolinovd, M., Fiala, J., Hampl, P., and Hlavaty, K. (2000, January 5\u20139). Satellite image processing and air pollution detection. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Istanbul, Turkey."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"25939","DOI":"10.1109\/ACCESS.2018.2833501","article-title":"A Review on Human-Centered IoT-Connected Smart Labels for the Industry 4.0","volume":"6","year":"2018","journal-title":"IEEE Access."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Jeong, S., Na, W., Kim, J., and Cho, S. (2018). Internet of Things for Smart Manufacturing Systems: Trust Issues in Resource Allocation. IEEE Internet Things J.","DOI":"10.1109\/JIOT.2018.2814063"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1109\/MCOM.2018.1700629","article-title":"Toward Dynamic Resources Management for IoT-Based Manufacturing","volume":"56","author":"Wan","year":"2018","journal-title":"IEEE Commun. Mag."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MSMC.2017.2702391","article-title":"The Internet of Things in Manufacturing: Key Issues and Potential Applications","volume":"4","author":"Yang","year":"2018","journal-title":"IEEE Syst. Man Cybern. Mag."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/JIOT.2018.2873426","article-title":"RFID-Based Object-Centric Data Management Framework for Smart Manufacturing Applications","volume":"6","author":"Meng","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Khan, T. (2018, January 3\u20135). A Cloud-Based Smart Expiry System Using QR Code. Proceedings of the 2018 IEEE International Conference on Electro\/Information Technology (EIT), Rochester, MI, USA.","DOI":"10.1109\/EIT.2018.8500140"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Liu, Y., and Gao, H. (2018). Traceability Management for the Food Safety along the Supply Chain Collaboration of Agricultural Products, No. 2.","DOI":"10.11648\/j.aff.20180702.13"},{"key":"ref_22","first-page":"32","article-title":"Research on the Status Quo and Supervision Mechanism of Food Safety in China","volume":"10","author":"Dong","year":"2018","journal-title":"Asian Agric. Res."},{"key":"ref_23","first-page":"83","article-title":"Vision navigation AGV system based on QR code","volume":"38","author":"Qing","year":"2019","journal-title":"Transducer Microsyst. Technol."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Van Parys, R., Verbandt, M., Kotz\u00e9, M., Coppens, P., Swevers, J., Bruyninckx, H., and Pipeleers, G. (2018, January 24\u201327). Distributed Coordination, Transportation & Localization in Industry 4.0. Proceedings of the 2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Nantes, France.","DOI":"10.1109\/IPIN.2018.8533768"},{"key":"ref_25","unstructured":"Meng, J., Kuo, C., and Chang, N.Y. (2016, January 8\u201310). Vision-based range finder for automated guided vehicle navigation. Proceedings of the 2016 IEEE Workshop on Advanced Robotics and its Social Impacts (ARSO), Shanghai, China."},{"key":"ref_26","unstructured":"Kumar, V.S.C., Sinha, A., Mallya, P.P., and Nath, N. (2017, January 14\u201316). An Approach towards Automated Navigation of Vehicles Using Overhead Cameras. Proceedings of the 2017 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Coimbatore, India."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2708","DOI":"10.1109\/TITS.2018.2790264","article-title":"Obstacle Prediction for Automated Guided Vehicles Based on Point Clouds Measured by a Tilted LIDAR Sensor","volume":"19","author":"Rozsa","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Romera-Paredes, B., and Torr, P.H.S. (2016). Recurrent Instance Segmentation. European Conference on Computer Vision, Springer International Publishing.","DOI":"10.1007\/978-3-319-46466-4_19"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Dollar, P., and Girshick, R. (2018). Mask R-CNN. IEEE Trans. Pattern Anal. Mach. Intell.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). UNet: Convolutional networks for biomedical image segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"546","DOI":"10.1049\/iet-ipr.2017.0677","article-title":"Fast detection method of quick response code based on run-length coding","volume":"12","author":"Li","year":"2018","journal-title":"IET Image Process."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhang, X., Luo, H., and Peng, J. (2017, January 23\u201325). Fast QR code detection. Proceedings of the 2017 International Conference on the Frontiers and Advances in Data Science (FADS), Xi\u2019an, China.","DOI":"10.1109\/FADS.2017.8253216"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/s11554-013-0325-6","article-title":"Real-time precise detection of regular grids and matrix codes","volume":"11","author":"Herout","year":"2016","journal-title":"J. Real Time Image Process."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Li, J.H., Wang, W.H., Rao, T.T., Zhu, W.B., and Liu, C.J. (2016, January 24\u201326). Morphological segmentation of 2-D barcode gray scale image. Proceedings of the 2016 International Conference on Information System and Artificial Intelligence, Hong Kong, China.","DOI":"10.1109\/ISAI.2016.0022"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Gr\u00f3sz, T., Bodn\u00e1r, P., T\u00f3th, L., and Ny\u00fal, L.G. (2014, January 21\u201324). QR code localization using deep neural networks. Proceedings of the 2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP), Reims, France.","DOI":"10.1109\/MLSP.2014.6958902"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Chou, T.H., Ho, C.S., and Kuo, Y.F. (2015, January 29\u201331). QR code detection using convolutional neural networks. Proceedings of the 2015 International Conference on Advanced Robotics and Intelligent Systems (ARIS), Taipei, Taiwan.","DOI":"10.1109\/ARIS.2015.7158354"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Lin, Y.-L., and Sung, C.-M. (2015, January 13\u201315). Preliminary study on QR code detection using HOG and AdaBoost. Proceedings of the 2015 7th International Conference of Soft Computing and Pattern Recognition (SoCPaR), Fukuoka, Japan.","DOI":"10.1109\/SOCPAR.2015.7492766"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Yuan, B., Li, Y., Jiang, F., Xu, X., Zhao, J., Zhang, D., Guo, J., Wang, Y., and Zhang, S. (2019, January 26\u201329). Fast QR code detection based on BING and AdaBoost-SVM. Proceedings of the 2019 IEEE 20th International Conference on High Performance Switching and Routing (HPSR), Xi\u2019An, China.","DOI":"10.1109\/HPSR.2019.8808000"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zitnick, C.L., and Doll\u00e1r, P. (2014). Edge boxes: Locating object proposals from edges [M]. Computer Vision-ECCV 2014, Springer.","DOI":"10.1007\/978-3-319-10602-1_26"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"814","DOI":"10.1109\/TPAMI.2015.2465908","article-title":"What makes for effective detection proposals?","volume":"38","author":"Hosang","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_41","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster R-CNN: Towards real-time object detection with region proposal networks. Proceedings of the International Conference on Neural Information Processing Systems, MIT Press."},{"key":"ref_42","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, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA. Available online: https:\/\/arxiv.org\/abs\/1612.08242.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_44","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 10\u201316). SSD: Single Shot MultiBox Detector. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_46","unstructured":"Pinheiro, P.O., Collobert, R., and Dollar, P. (2015, January 7\u201312). Learning to segment object candidates. Proceedings of the 28th International Conference on Neural Information Processing Systems, NIPS\u201915, Montreal, QC, Canada."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Pinheiro, P.O., Lin, T.Y., Collobert, R., and Dollar, P. (2016). Learning to refine object segments. Proceedings of the European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-46448-0_5"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Dai, J.F., He, K.M., Li, Y., Ren, S.Q., and Sun, J. (2016). Instance-sensitive fully convolutional networks. Proceedings of the European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-46466-4_32"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Li, Y., Qi, H.Z., Dai, J.F., Ji, X.Y., and Wei, Y.C. (2017, January 21\u201326). Fully convolutional instance-aware semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.472"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.sysarc.2015.11.006","article-title":"Real-time continuous feature extraction in large size satellite images","volume":"64","author":"Rathore","year":"2016","journal-title":"J. Syst. Archit. EUROMICRO"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1007\/s42835-018-00012-w","article-title":"Depth maps-based human segmentation and action recognition using full-body plus body color cues via recognizer engine","volume":"14","author":"Jalal","year":"2019","journal-title":"J. Electr. Eng. Technol."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Mahmood, M., Jalal, A., and Evans, A.H. (2018, January 4\u20135). Facial Expression Recognition in Image Sequences Using 1D Transform and Gabor Wavelet Transform. Proceedings of the IEEE conference on International Conference on Applied and Engineering Mathematics, Taxila, Pakistan.","DOI":"10.1109\/ICAEM.2018.8536280"},{"key":"ref_53","unstructured":"Yoshimoto, H., Date, N., and Yonemoto, S. (2003, January 1). Vision-based real-time motion capture system using multiple cameras. Proceedings of the IEEE Conference on Multisensor Fusion and Integration for Intelligent Systems, Tokyo, Japan."},{"key":"ref_54","first-page":"1189","article-title":"Detecting Complex 3D Human Motions with Body Model Low-Rank Representation for Real-Time Smart Activity Monitoring System","volume":"12","author":"Jalal","year":"2018","journal-title":"KSII Trans. Internet Inf. Syst."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Farooq, F., Ahmed, J., and Zheng, L. (2017, January 10\u201314). Facial expression recognition using hybrid features and self-organizing maps. Proceedings of the IEEE International Conference on Multimedia and Expo, Hong Kong, China.","DOI":"10.1109\/ICME.2017.8019503"},{"key":"ref_56","unstructured":"Huang, Q., Yang, J., and Qiao, Y. (November, January 30). Person re-identification across multi-camera system based on local descriptors. Proceedings of the IEEE Conference on Distributed Smart Cameras, Hong Kong, China."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Piyathilaka, L., and Kodagoda, S. (2013, January 19\u201321). Gaussian mixture based HMM for human daily activity recognition using 3D skeleton features. Proceedings of the International Conference on Industrial Electronics and Applications (ICIEA), Melbourne, VIC, Australia.","DOI":"10.1109\/ICIEA.2013.6566433"},{"key":"ref_58","first-page":"54","article-title":"A depth video-based human detection and activity recognition using multi-features and embedded hidden Markov models for health care monitoring systems","volume":"4","author":"Jalal","year":"2017","journal-title":"Int. J. Interact. Multimed. Artif. Intell."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.patcog.2016.08.003","article-title":"Robust human activity recognition from depth video using spatiotemporal multi-fused features","volume":"61","author":"Jalal","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Jalal, A., Kamal, S., and Kim, D. (2015, January 28\u201330). Individual Detection-Tracking-Recognition using depth activity images. Proceedings of the 12th IEEE International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), Goyang, Korea.","DOI":"10.1109\/URAI.2015.7358903"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1921","DOI":"10.5370\/JEET.2016.11.6.1857","article-title":"Depth Images-based Human Detection, Tracking and Activity Recognition Using Spatiotemporal Features and Modified HMM","volume":"11","author":"Kamal","year":"2016","journal-title":"J. Electr. Eng. Technol."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2016\/8087545","article-title":"Human depth sensors-based activity recognition using spatiotemporal features and hidden markov model for smart environments","volume":"2016","author":"Jalal","year":"2016","journal-title":"J. Comput. Netw. Commun."},{"key":"ref_63","first-page":"1657","article-title":"Facial Expression recognition using 1D transform features and Hidden Markov Model","volume":"12","author":"Jalal","year":"2017","journal-title":"J. Electr. Eng. Technol."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Wu, H., Pan, W., Xiong, X., and Xu, S. (2014, January 28\u201330). Human activity recognition based on the combined SVM & HMM. Proceedings of the International Conference on Information and Automation, Hailar, China.","DOI":"10.1109\/ICInfA.2014.6932656"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1007\/s13369-015-1955-8","article-title":"A hybrid feature extraction approach for human detection, tracking and activity recognition using depth sensors","volume":"41","author":"Kamal","year":"2016","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Jalal, A. (2015, January 28\u201330). Depth Silhouettes Context: A new robust feature for human tracking and activity recognition based on embedded HMMs. Proceedings of the 12th IEEE International Conference on Ubiquitous Robots and Ambient Intelligence, Goyang, Korea.","DOI":"10.1109\/URAI.2015.7358957"},{"key":"ref_67","first-page":"1856","article-title":"Dense RGB-D Map-Based Human Tracking and Activity Recognition using Skin Joints Features and Self-Organizing Map","volume":"9","author":"Farooq","year":"2015","journal-title":"KSII Trans. Internet Inf. Syst."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Jalal, A., Kamal, S., and Farooq, A. (2015, January 15\u201318). A spatiotemporal motion variation features extraction approach for human tracking and pose-based action recognition. Proceedings of the IEEE International Conference on Informatics, Electronics and Vision, Fukuoka, Japan.","DOI":"10.1109\/ICIEV.2015.7334049"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Huang, Z., Huang, L., Gong, Y., Huang, C., and Wang, X. (2019, January 16\u201320). Mask Scoring R-CNN. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition CVPR, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00657"},{"key":"ref_70","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-scale Image Recognition. arXiv."},{"key":"ref_71","unstructured":"Crimi, A., Bakas, S., Kuijf, H., Menze, B., and Reyes, M. (2018). Generalised Wasserstein dice score for imbalanced multi-class segmentation using holistic convolutional networks. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2017, Springer. Lecture Notes in Computer Science."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Bebis, G., Boyle, R., Parvin, B., Koracin, D., Porikli, F., Skaff, S., Entezari, A., Min, J., Iwai, D., and Sadagic, A. (2016). Optimizing intersection-over-union in deep neural networks for image segmentation. Advances in Visual Computing. ISVC 2016, Springer. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-319-50835-1"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., and Ahmadi, S.-A. (2016, January 25\u201328). V-net: Fully convolutional neural networks for volumetric medical image segmentation. Proceedings of the 2016 Fourth International Conference on 3D Vision (3DV), Stanford, CA, USA.","DOI":"10.1109\/3DV.2016.79"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Yang, T., Ren, Q., Zhang, F., Xie, B., Ren, H., Li, J., and Zhang, Y. (2018). Hybrid Camera Array-Based UAV Auto-Landing on Moving UGV in GPS-Denied Environment. Remote Sens., 10.","DOI":"10.3390\/rs10111829"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Mueller, M., Smith, N., and Ghanem, B. (2016, January 10\u201316). A Benchmark and Simulator for UAV Tracking. Proceedings of the 2016 European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_27"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1007\/s10846-017-0483-z","article-title":"Survey on Computer Vision for UAVs: Current Developments and Trends","volume":"87","author":"Kanellakis","year":"2017","journal-title":"J. Intell. Robot Syst."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Yang, T., Li, G., Li, J., Zhang, Y., Zhang, X., Zhang, Z., and Li, Z. (2016). A Ground-Based Near Infrared Camera Array System for UAV Auto-Landing in GPS-Denied Environment. Sensors, 16.","DOI":"10.3390\/s16091393"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1051\/jnwpu\/20183620294","article-title":"Real-time Detection and Tracking Method of Landmark Based on UAV Visual Navigation","volume":"36","author":"Li","year":"2018","journal-title":"J. Northwestern Poly Tech. Univ."},{"key":"ref_79","unstructured":"Sharp, S., Shakernia, O., and Shankar, S. (2001, January 21\u201326). A vision System for Landing an Unmanned Aerial Vehicle. Proceedings of the IEEE International Conference on Robotics and Automation, Seoul, Korea."},{"key":"ref_80","unstructured":"Sven, L., Niko, S., and Peter, P. (2009, January 22\u201326). A vision based onboard approach for landing and position control of an autonomous multirotor UAV in GPS-denied environments. Proceedings of the International Conference on Advanced Robotics, Munich, Germany."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1007\/s10514-016-9564-2","article-title":"Monocular vision based real-time target recognition and tracking for autonomously landing an UAV in a cluttered shipboard environment","volume":"41","author":"Lin","year":"2017","journal-title":"Auton. Robot."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1007\/s10846-016-0399-z","article-title":"Vision based autonomous landing of multirotor UAV on moving platform","volume":"85","author":"Araar","year":"2017","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.engappai.2016.10.016","article-title":"Vision-based control of a quadrotor utilizing artificial neural networks for tracking of moving targets","volume":"58","author":"Shirzadeh","year":"2017","journal-title":"Eng. Appl. Artif. Intell."}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/11\/9\/197\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:19:59Z","timestamp":1760188799000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/11\/9\/197"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,13]]},"references-count":83,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2019,9]]}},"alternative-id":["fi11090197"],"URL":"https:\/\/doi.org\/10.3390\/fi11090197","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,9,13]]}}}