{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T20:53:43Z","timestamp":1780952023406,"version":"3.54.1"},"reference-count":39,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,11,30]],"date-time":"2025-11-30T00:00:00Z","timestamp":1764460800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001459","name":"Singapore MOE","doi-asserted-by":"publisher","award":["RG100\/23"],"award-info":[{"award-number":["RG100\/23"]}],"id":[{"id":"10.13039\/501100001459","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001475","name":"NTU","doi-asserted-by":"publisher","award":["03INS001984C130"],"award-info":[{"award-number":["03INS001984C130"]}],"id":[{"id":"10.13039\/501100001475","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Surveillance cameras are extensively deployed across public and private environments, driving the need for intelligent video monitoring systems. However, a major challenge arises in Weakly Supervised Video Anomaly Detection (WSVAD), where supervision is limited to video-level labels, making snippet-level anomaly localisation particularly difficult. This challenge is often formulated as a Multiple Instance Learning (MIL) problem. Although recent approaches have achieved encouraging results by modelling spatio-temporal dynamics, they often overlook the semantic information within videos that could further enhance anomaly detection. To bridge this gap, we propose enriching feature representations by applying object detection techniques to extract object-centric features. These features provide supplementary high-level semantic information that supports the discrimination of anomalous events. Experiments conducted on two benchmark datasets, UCF-Crime and ShanghaiTech, demonstrate that our approach achieves performance comparable to state-of-the-art (SOTA) methods. The results highlight that incorporating object-level semantics offers a promising direction for improving WSVAD, underscoring the potential of semantic-aware approaches for more effective anomaly detection.<\/jats:p>","DOI":"10.3390\/info16121042","type":"journal-article","created":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T08:13:49Z","timestamp":1764576829000},"page":"1042","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Enhancing Weakly Supervised Video Anomaly Detection with Object-Centric Features"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5226-2537","authenticated-orcid":false,"given":"Yanyu","family":"Wang","sequence":"first","affiliation":[{"name":"College of Computing and Data Science, Nanyang Technological University, Singapore 639768, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8697-003X","authenticated-orcid":false,"given":"Yang","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Computing and Data Science, Nanyang Technological University, Singapore 639768, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7618-1472","authenticated-orcid":false,"given":"Chai Kiat","family":"Yeo","sequence":"additional","affiliation":[{"name":"College of Computing and Data Science, Nanyang Technological University, Singapore 639768, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1145\/3439950","article-title":"Deep Learning for Anomaly Detection: A Review","volume":"54","author":"Pang","year":"2022","journal-title":"ACM Comput. Surv."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1007\/s11235-018-0475-8","article-title":"A comprehensive survey on network anomaly detection","volume":"70","author":"Fernandes","year":"2019","journal-title":"Telecommun. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"116429","DOI":"10.1016\/j.eswa.2021.116429","article-title":"Financial fraud: A review of anomaly detection techniques and recent advances","volume":"193","author":"Hilal","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1145\/3464423","article-title":"Deep Learning for Medical Anomaly Detection\u2014A Survey","volume":"54","author":"Fernando","year":"2022","journal-title":"ACM Comput. Surv."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Avola, D., Cinque, L., Di Mambro, A., Diko, A., Fagioli, A., Foresti, G.L., Marini, M.R., Mecca, A., and Pannone, D. (2021). Low-Altitude Aerial Video Surveillance via One-Class SVM Anomaly Detection from Textural Features in UAV Images. Information, 13.","DOI":"10.3390\/info13010002"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Hasan, M., Choi, J., Neumann, J., Roy-Chowdhury, A.K., and Davis, L.S. (2016, January 27\u201330). Learning temporal regularity in video sequences. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.86"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Tian, Y., Pang, G., Chen, Y., Singh, R., Verjans, J.W., and Carneiro, G. (2021, January 11\u201317). Weakly-supervised video anomaly detection with robust temporal feature magnitude learning. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00493"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Carreira, J., and Zisserman, A. (2017, January 21\u201326). Quo vadis, action recognition? A new model and the kinetics dataset. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.502"},{"key":"ref_9","first-page":"387","article-title":"Mgfn: Magnitude-contrastive glance-and-focus network for weakly-supervised video anomaly detection","volume":"37","author":"Chen","year":"2023","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2023, January 17\u201324). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/978-3-031-72751-1_1","article-title":"YOLOv9: Learning What YouWant to Learn Using Programmable Gradient Information","volume":"Volume 15089","author":"Leonardis","year":"2025","journal-title":"Computer Vision\u2013ECCV 2024"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Liu, W., Luo, W., Lian, D., and Gao, S. (2018, January 18\u201323). Future frame prediction for anomaly detection\u2013a new baseline. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00684"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Sultani, W., Chen, C., and Shah, M. (2018, January 18\u201323). Real-world anomaly detection in surveillance videos. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00678"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Acsintoae, A., Florescu, A., Georgescu, M.I., Mare, T., Sumedrea, P., Ionescu, R.T., Khan, F.S., and Shah, M. (2022, January 18\u201324). Ubnormal: New benchmark for supervised open-set video anomaly detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01951"},{"key":"ref_15","unstructured":"Gong, D., Liu, L., Le, V., Saha, B., Mansour, M.R., Venkatesh, S., and Hengel, A.v.d. (November, January 27). Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_16","unstructured":"Zaheer, M.Z., Lee, J.h., Astrid, M., and Lee, S.I. (2020, January 13\u201319). Old is gold: Redefining the adversarially learned one-class classifier training paradigm. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Pang, G., Yan, C., Shen, C., Hengel, A.v.d., and Bai, X. (2020, January 13\u201319). Self-trained deep ordinal regression for end-to-end video anomaly detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01219"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhao, H., Jia, J., and Koltun, V. (2020, January 13\u201319). Exploring self-attention for image recognition. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01009"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Luo, W., Liu, W., and Gao, S. (2017, January 10\u201314). Remembering history with convolutional lstm for anomaly detection. Proceedings of the 2017 IEEE International Conference on Multimedia and Expo (ICME), Hong Kong, China.","DOI":"10.1109\/ICME.2017.8019325"},{"key":"ref_20","unstructured":"Nguyen, D.T., Lou, Z., Klar, M., and Brox, T. (2019, January 9\u201315). Anomaly detection with multiple-hypotheses predictions. Proceedings of the International Conference on Machine Learning, Long Beach, CA, USA."},{"key":"ref_21","unstructured":"Nguyen, T.N., and Meunier, J. (November, January 27). Anomaly detection in video sequence with appearance-motion correspondence. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Lv, H., Yue, Z., Sun, Q., Luo, B., Cui, Z., and Zhang, H. (2023, January 17\u201324). Unbiased multiple instance learning for weakly supervised video anomaly detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00775"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhong, J.X., Li, N., Kong, W., Liu, S., Li, T.H., and Li, G. (2019, January 15\u201320). Graph convolutional label noise cleaner: Train a plug-and-play action classifier for anomaly detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00133"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wan, B., Fang, Y., Xia, X., and Mei, J. (2020, January 6\u201310). Weakly supervised video anomaly detection via center-guided discriminative learning. Proceedings of the 2020 IEEE International Conference on Multimedia and Expo (ICME), London, UK.","DOI":"10.1109\/ICME46284.2020.9102722"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"9961","DOI":"10.1109\/TITS.2021.3096854","article-title":"A review and comparative study on probabilistic object detection in autonomous driving","volume":"23","author":"Feng","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Yang, R., and Yu, Y. (2021). Artificial convolutional neural network in object detection and semantic segmentation for medical imaging analysis. Front. Oncol., 11.","DOI":"10.3389\/fonc.2021.638182"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Raghunandan, A., Raghav, P., and Aradhya, H.R. (2018, January 3\u20135). Object detection algorithms for video surveillance applications. Proceedings of the 2018 International Conference on Communication and Signal Processing (ICCSP), Chennai, India.","DOI":"10.1109\/ICCSP.2018.8524461"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"9243","DOI":"10.1007\/s11042-022-13644-y","article-title":"Object detection using YOLO: Challenges, architectural successors, datasets and applications","volume":"82","author":"Diwan","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_29","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_30","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_31","first-page":"35","article-title":"Hydra Attention: Efficient Attention with Many Heads","volume":"Volume 13807","author":"Karlinsky","year":"2023","journal-title":"Computer Vision\u2013ECCV 2022 Workshops"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Luo, W., Liu, W., and Gao, S. (2017, January 22\u201329). A revisit of sparse coding based anomaly detection in stacked rnn framework. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.45"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Park, H., Noh, J., and Ham, B. (2020, January 13\u201319). Learning memory-guided normality for anomaly detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01438"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Yu, G., Wang, S., Cai, Z., Zhu, E., Xu, C., Yin, J., and Kloft, M. (2020, January 12\u201316). Cloze Test Helps: Effective Video Anomaly Detection via Learning to Complete Video Events. Proceedings of the 28th ACM International Conference on Multimedia, Seattle, WA, USA.","DOI":"10.1145\/3394171.3413973"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhang, J., Qing, L., and Miao, J. (2019, January 22\u201325). Temporal convolutional network with complementary inner bag loss for weakly supervised anomaly detection. Proceedings of the 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan.","DOI":"10.1109\/ICIP.2019.8803657"},{"key":"ref_36","unstructured":"Wang, J., and Cherian, A. (November, January 27). Gods: Generalized one-class discriminative subspaces for anomaly detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zaheer, M.Z., Mahmood, A., Khan, M.H., Segu, M., Yu, F., and Lee, S.I. (2022, January 18\u201324). Generative cooperative learning for unsupervised video anomaly detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01433"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1007\/978-3-030-58577-8_20","article-title":"Not only Look, But Also Listen: Learning Multimodal Violence Detection Under Weak Supervision","volume":"Volume 12375","author":"Vedaldi","year":"2020","journal-title":"Computer Vision\u2013ECCV 2020"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Doshi, K., and Yilmaz, Y. (2020, January 13\u201319). Continual learning for anomaly detection in surveillance videos. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00135"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/12\/1042\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T09:10:01Z","timestamp":1764580201000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/12\/1042"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,30]]},"references-count":39,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["info16121042"],"URL":"https:\/\/doi.org\/10.3390\/info16121042","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,30]]}}}