{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T18:57:44Z","timestamp":1785956264081,"version":"3.56.0"},"reference-count":35,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,15]],"date-time":"2022-11-15T00:00:00Z","timestamp":1668470400000},"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 (NRF)","doi-asserted-by":"publisher","award":["2021R1I1A3055973"],"award-info":[{"award-number":["2021R1I1A3055973"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Soonchunhyang University Research Fund","award":["2021R1I1A3055973"],"award-info":[{"award-number":["2021R1I1A3055973"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Economic and social progress in the Republic of Korea resulted in an increased standard of living, which subsequently produced more waste. The Korean government implemented a volume-based trash disposal system that may modify waste disposal characteristics to handle vast volumes of waste efficiently. However, the inconvenience of having to purchase standard garbage bags on one\u2019s own led to passive participation by citizens and instances of illegally dumping waste in non-standard plastic bags. As a result, there is a need for the development of automatic detection and reporting of illegal acts of garbage dumping. To achieve this, we suggest a system for tracking unlawful rubbish disposal that is based on deep neural networks. The proposed monitoring approach obtains the articulation points (joints) of a dumper through OpenPose and identifies the type of garbage bag through the object detection model, You Only Look Once (YOLO), to determine the distance of the dumper\u2019s wrist to the garbage bag and decide whether it is illegal dumping. Additionally, we introduced a method of tracking the IDs issued to the waste bags using the multi-object tracking (MOT) model to reduce the false detection of illegal dumping. To evaluate the efficacy of the proposed illegal dumping monitoring system, we compared it with the other systems based on behavior recognition. As a result, it was validated that the suggested approach had a higher degree of accuracy and a lower percentage of false alarms, making it useful for a variety of upcoming applications.<\/jats:p>","DOI":"10.3390\/s22228819","type":"journal-article","created":{"date-parts":[[2022,11,16]],"date-time":"2022-11-16T04:39:03Z","timestamp":1668573543000},"page":"8819","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["AIDM-Strat: Augmented Illegal Dumping Monitoring Strategy through Deep Neural Network-Based Spatial Separation Attention of Garbage"],"prefix":"10.3390","volume":"22","author":[{"given":"Yeji","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Soonchunhyang University, Asan 31538, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5162-1745","authenticated-orcid":false,"given":"Jeongho","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Soonchunhyang University, Asan 31538, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,15]]},"reference":[{"key":"ref_1","unstructured":"Park, J. (2000). An Evaluation of Volume-Based Waste Collection Fee System. [Master\u2019s Thesis, Kyunghee University]."},{"key":"ref_2","unstructured":"Kim, D. (2006). A Study on Improvement Plan for Trash Specific Duty. [Master\u2019s Thesis, Chosun University]."},{"key":"ref_3","unstructured":"Kim, J. (2003). A Study on the Estimation for Improvement of the Volume based Waste Fee System. [Master\u2019s Thesis, Chung-Ang University]."},{"key":"ref_4","unstructured":"Seoul Information Communication Plaza (2022, September 01). Performance of Cracking Down on Illegal Dumping of Garbage. Available online: https:\/\/opengov.seoul.go.kr\/."},{"key":"ref_5","unstructured":"Mu, J. (2016). A Study on Improving Household Waste Collection Systems. [Master\u2019s Thesis, Chung-Ang University]."},{"key":"ref_6","first-page":"1508","article-title":"Garbage Dumping Detection System using Articular point Deep Learning","volume":"24","author":"Min","year":"2021","journal-title":"J. Korea Multimed. Soc."},{"key":"ref_7","unstructured":"Bae, C., Kim, H., Yeo, J., Jeong, J., and Yun, T. (2020, January 16\u201318). Development of Monitoring System for Detecting Illegal Dumping Using Deep Learning. Proceedings of the Korean Society of Computer Information, Jeju, Korea."},{"key":"ref_8","unstructured":"Jeong, J., Kwon, S., Kim, Y., Hong, S., and Kim, Y. (2017, January 18\u201320). Development of Illegal Dumping System using Image Processing. Proceedings of the Korean Institute of Information Scientists and Engineers, Jeju, Korea."},{"key":"ref_9","unstructured":"Kim, J., Kim, H., Kim, P., and Lee, Y. (2017, January 18\u201320). The Design of Intelligent System for Statistically Determining Illegal Garbage Dumping through Trajectory Analysis. Proceedings of the Korean Institute of Information Scientists and Engineers, Jeju, Korea."},{"key":"ref_10","unstructured":"Ramanan, D., Forsyth, D., and Zisserman, A. (2005, January 20\u201325). Tracking people and recognizing their activities. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2878","DOI":"10.1109\/TPAMI.2012.261","article-title":"Articulated Human Detection with Flexible Mixtures of Parts","volume":"35","author":"Yang","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_12","unstructured":"Lan, X., and Huttenlocher, D. (2005, January 17\u201321). Beyond trees: Common-factor models for 2D human pose recovery. Proceedings of the IEEE International Conference on Computer Vision, Beijing, China."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Dantone, M., Gall, J., Leistner, C., and Van Gool, L. (2013, January 23\u201328). Human Pose Estimation Using Body Parts Dependent Joint Regressors. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.391"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Tompson, J., Goroshin, R., Jain, A., LeCun, Y., and Bregler, C. (2015, January 7\u201312). Efficient Object Localization using Convolutional Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298664"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Chen, Y., Shen, C., Wei, X.-S., Liu, L., and Yang, J. (2017, January 22\u201329). Adversarial PoseNet: A Structure-Aware Convolutional Network for Human Pose Estimation. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.137"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Sun, M., and Savarese, M. (2011, January 6\u201313). Articulated part-based model for joint object detection and pose estimation. Proceedings of the IEEE International Conference on Computer Vision, Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126309"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Fang, H.-S., Xie, S., Tai, Y.-W., and Lu, C. (2017, January 22\u201329). RMPE: Regional Multi-person Pose Estimation. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.256"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1109\/TPAMI.2019.2929257","article-title":"OpenPose: Realtime multi-person 2D pose estimation using part affinity fields","volume":"43","author":"Cao","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_19","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_20","doi-asserted-by":"crossref","unstructured":"Wojke, N., Bewley, A., and Paulus, D. (2017, January 17-\u201320). Simple Online Realtime Tracking with a Deep Association Metric. Proceedings of the IEEE Conference on Image Processing, Beijing, China.","DOI":"10.1109\/ICIP.2017.8296962"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Badave, H., and Kuber, M. (2021, January 6\u20138). Evaluation of Person Recognition Accuracy based on OpenPose Parameters. Proceedings of the International Conference on Intelligent Computing and Control Systems, Madurai, India.","DOI":"10.1109\/ICICCS51141.2021.9432108"},{"key":"ref_22","unstructured":"Simonyan, K., and Zisserman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Hosang, J., Benenson, R., and Schiele, B. (2017, January 21\u201326). Learning Non-maximum Suppression. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.685"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume":"39","author":"Ren","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"He, K., Georgia, G., Dollar, P., and Girshick, R. (2017, January 22\u201329). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 11\u201314). 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_29","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Akyol, G., Kantarc\u0131, A., \u00c7elik, A., and Cihan Ak, A. (2020, January 5\u20137). Deep Learning Based, Real-Time Object Detection for Autonomous Driving. Proceedings of the IEEE Conference on Signal Processing and Communications Applications, Gaziantep, Turkey.","DOI":"10.1109\/SIU49456.2020.9302500"},{"key":"ref_31","unstructured":"Teknomo, K., Takeyama, Y., and Inaura, H. (2001, January 8). Frame-based tracing of multiple objects. Proceedings of the IEEE Workshop on Multi-Object Tracking, Vancouver, BC, Canada."},{"key":"ref_32","unstructured":"Luo, W., Xing, J., Milan, A., Zhang, X., Liu, W., and Kim, T.K. (2017). Multiple Object Tracking: A Literature Review. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zheng, L., Liu, Y., Li, Y., and Wang, S. (2020, January 23\u201328). Towards Real-Time Multi-Object Tracking. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58621-8_7"},{"key":"ref_34","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, Phoenix, AZ, USA.","DOI":"10.1109\/ICIP.2016.7533003"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Pereira, R., Carvalho, G., Garrote, L., and Nunes, U.J. (2022). Sort and Deep-SORT Based Multi-Object Tracking for Mobile Robotics: Evaluation with New Data Association Metrics. Appl. 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