{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T19:34:24Z","timestamp":1781379264178,"version":"3.54.1"},"reference-count":23,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2020,4,3]],"date-time":"2020-04-03T00:00:00Z","timestamp":1585872000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41861134010"],"award-info":[{"award-number":["41861134010"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Basic scientific research project of Heilongjiang Province","award":["KJCXZD201704"],"award-info":[{"award-number":["KJCXZD201704"]}]},{"name":"Key Laboratory of Police Wireless Digital Communication, Ministry of Public Security","award":["2018JYWXTX01"],"award-info":[{"award-number":["2018JYWXTX01"]}]},{"name":"Finnish Cultural Foundation, North-Ostrobothnia Regional Fund","award":["2017"],"award-info":[{"award-number":["2017"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>School bullying is a serious problem among teenagers. School violence is one type of school bullying and considered to be the most harmful. As AI (Artificial Intelligence) techniques develop, there are now new methods to detect school violence. This paper proposes a video-based school violence detecting algorithm. This algorithm first detects foreground moving targets via the KNN (K-Nearest Neighbor) method and then preprocesses the detected targets via morphological processing methods. Then, this paper proposes a circumscribed rectangular frame integrating method to optimize the circumscribed rectangular frame of moving targets. Rectangular frame features and optical-flow features were extracted to describe the differences between school violence and daily-life activities. We used the Relief-F and Wrapper algorithms to reduce the feature dimension. SVM (Support Vector Machine) was applied as the classifier, and 5-fold cross validation was performed. The accuracy was 89.6%, and the precision was 94.4%. To further improve the recognition performance, we developed a DT\u2013SVM (Decision Tree\u2013SVM) two-layer classifier. We used boxplots to determine some features of the DT layer that are able to distinguish between typical physical violence and daily-life activities and between typical daily-life activities and physical violence. For the remainder of activities, the SVM layer performed a classification. For this DT\u2013SVM classifier, the accuracy reached 97.6%, and the precision reached 97.2%, thus showing a significant improvement.<\/jats:p>","DOI":"10.3390\/s20072018","type":"journal-article","created":{"date-parts":[[2020,4,7]],"date-time":"2020-04-07T03:58:39Z","timestamp":1586231919000},"page":"2018","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["A Video-Based DT\u2013SVM School Violence Detecting Algorithm"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6076-0261","authenticated-orcid":false,"given":"Liang","family":"Ye","sequence":"first","affiliation":[{"name":"Department of Information and Communication Engineering, Harbin Institute of Technology, Harbin150080, China"},{"name":"OPEM Research Unit, University of Oulu, 90014 Oulu, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Le","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Engineering, Harbin Institute of Technology, Harbin150080, China"},{"name":"Huawei Beijing Institute, Beijing 100085, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0857-2946","authenticated-orcid":false,"given":"Hany","family":"Ferdinando","sequence":"additional","affiliation":[{"name":"OPEM Research Unit, University of Oulu, 90014 Oulu, Finland"},{"name":"Department of Electrical Engineering, Petra Christian University, Surabaya 60236, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3963-0750","authenticated-orcid":false,"given":"Tapio","family":"Sepp\u00e4nen","sequence":"additional","affiliation":[{"name":"Physiological Signal Analysis Team, University of Oulu, 90014 Oulu, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Esko","family":"Alasaarela","sequence":"additional","affiliation":[{"name":"OPEM Research Unit, University of Oulu, 90014 Oulu, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"740358","DOI":"10.1155\/2014\/740358","article-title":"Physical violence detection for preventing school bullying","volume":"2014","author":"Ye","year":"2014","journal-title":"Adv. Artif. Intell."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ye, L., Ferdinando, H., Sepp\u00e4nen, T., Huuki, T., and Alasaarela, E. (2015, January 24\u201328). An instance-based physical violence detection algorithm for school bullying prevention. Proceedings of the 2015 International Wireless Communications and Mobile Computing Conference (IWCMC), Dubrovnik, Croatia.","DOI":"10.1109\/IWCMC.2015.7289284"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1850046","DOI":"10.1142\/S0218001418500465","article-title":"A combined motion-audio school bullying detection algorithm","volume":"32","author":"Ye","year":"2018","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ye, L., Shi, J., Ferdinando, H., Sepp\u00e4nen, T., and Alasaarela, E. (2019, January 25\u201326). School violence detection based on multi-sensor fusion and improved Relief-F algorithms. Proceedings of the International Conference on Artificial Intelligence for Communications and Networks 2019, Harbin, China.","DOI":"10.1007\/978-3-030-22971-9_22"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zainudin, M.S., Sulaiman, M.N., Mustapha, N., and Perumal, T. (2018, January 21\u201322). Activity recognition using one-versus-all strategy with Relief-F and self-adaptive algorithm. Proceedings of the 2018 IEEE Conference on Open Systems (ICOS), Langkawi Island, Malaysia.","DOI":"10.1109\/ICOS.2018.8632818"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Bhavan, A., and Aggarwal, S. (2018, January 18\u201321). Stacked generalization with wrapper-based feature selection for human activity recognition. Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence (SSCI), Bangalore, India.","DOI":"10.1109\/SSCI.2018.8628830"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Mohammed, N.N., Khaleel, M.I., Latif, M., and Khalid, Z. (2018, January 21\u201324). Face recognition based on PCA with weighted and normalized Mahalanobis distance. Proceedings of the 2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), Bangkok, Thailand.","DOI":"10.1109\/ICIIBMS.2018.8549971"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Aburomman, A.A., and Reaz, M.B.I. (2016, January 3\u20135). Ensemble of binary SVM classifiers based on PCA and LDA feature extraction for intrusion detection. Proceedings of the 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), Xi\u2019an, China.","DOI":"10.1109\/IMCEC.2016.7867287"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1109\/MNET.2019.1800305","article-title":"Full-duplex relay-assisted macrocell with millimeter wave backhauls: Framework and prospects","volume":"33","author":"Han","year":"2019","journal-title":"IEEE Netw."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"31314","DOI":"10.3390\/s151229858","article-title":"Physical human activity recognition using wearable sensors","volume":"15","author":"Attal","year":"2015","journal-title":"Sensors"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1585","DOI":"10.1109\/TII.2017.2777846","article-title":"Collaborative energy and information transfer in green wireless sensor networks for smart cities","volume":"14","author":"Lu","year":"2017","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1109\/ACCESS.2017.2761910","article-title":"Multi-modal cooperative spectrum sensing based on dempster-shafer fusion in 5G-based cognitive radio","volume":"6","author":"Liu","year":"2018","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Sun, X., Huang, D., Wang, Y., and Qin, J. (2014, January 27\u201330). Action recognition based on kinematic representation of video data. Proceedings of the 2014 IEEE International Conference on Image Processing (ICIP), Paris, France.","DOI":"10.1109\/ICIP.2014.7025306"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1109\/TIP.2013.2292550","article-title":"Action recognition using nonnegative action component representation and sparse basis selection","volume":"23","author":"Wang","year":"2014","journal-title":"IEEE Trans. Image Process."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2799","DOI":"10.1109\/TIP.2018.2890749","article-title":"Action-stage emphasized spatiotemporal VLAD for video action recognition","volume":"28","author":"Tu","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.1049\/el.2017.0970","article-title":"Violent activity detection with transfer learning method","volume":"53","author":"Kaya","year":"2017","journal-title":"Electron. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ha, J., Park, J., Kim, H., Park, H., and Paik, J. (2018, January 24\u201327). Violence detection for video surveillance system using irregular motion information. Proceedings of the 2018 International Conference on Electronics, Information, and Communication (ICEIC), Honolulu, HI, USA.","DOI":"10.23919\/ELINFOCOM.2018.8330609"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ehsan, T.Z., and Nahvi, M. (2018, January 25\u201326). Violence detection in indoor surveillance cameras using motion trajectory and differential histogram of optical flow. Proceedings of the 2018 8th International Conference on Computer and Knowledge Engineering (ICCKE), Mashhad, Iran.","DOI":"10.1109\/ICCKE.2018.8566460"},{"key":"ref_19","unstructured":"Shu, Z., Nannan, C., Chao, F., and Shaosheng, L. (2011, January 8\u201310). The design and implementation of automatic camera servo system based on improved frame differential algorithm. Proceedings of the 2011 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC), Dengleng, China."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Tian, Y., Deng, L., and Li, Q. (2017, January 15\u201318). A KNN match based tracking-learning-detection method with adjustment of surveyed areas. Proceedings of the 2017 13th International Conference on Computational Intelligence and Security (CIS), Hong Kong, China.","DOI":"10.1109\/CIS.2017.00104"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Benraya, I., and Benblidia, N. (2018, January 24\u201325). Comparison of background subtraction methods. Proceedings of the 2018 International Conference on Applied Smart Systems (ICASS), Medea, Algeria.","DOI":"10.1109\/ICASS.2018.8652040"},{"key":"ref_22","unstructured":"Gao, W., Zhang, X., Yang, L., and Liu, H. (2010, January 9\u201311). An improved sobel edge detection. Proceedings of the 2010 3rd international conference on computer science and information technology, Chengdu, China."},{"key":"ref_23","first-page":"1","article-title":"Passenger violence detection based on elevator video","volume":"31","author":"Ji","year":"2018","journal-title":"Ind. Control Comput."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/7\/2018\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:15:18Z","timestamp":1760174118000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/7\/2018"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,3]]},"references-count":23,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2020,4]]}},"alternative-id":["s20072018"],"URL":"https:\/\/doi.org\/10.3390\/s20072018","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,4,3]]}}}