Lightweight deep models for video anomaly detection: a comparative study of autoencoders and MobileNetV2 on the avenue dataset
DOI: https://doi.org/10.3846/ntcs.2025.25567Abstract
Video anomaly detection aims to identify unusual events in surveillance footage, yet many existing deep learning solutions remain too computationally heavy for real-time deployment on resource-limited hardware. This study presents a systematic comparison of three lightweight deep learning models for frame-level anomaly detection on the Avenue dataset, including a baseline 2D convolutional autoencoder, an enhanced reconstruction-based autoencoder with refined feature representation and decoding strategy, and a MobileNetV2-based supervised classifier fine-tuned for anomaly recognition. The baseline autoencoder achieves moderate detection performance, with an approximately AUC of 0.75. In contrast, the enhanced autoencoder improves reconstruction quality and raises the AUC to approximately 0.84 through more effective feature abstraction rather than increased architectural depth. The strongest results are obtained by the MobileNetV2 classifier, which achieves an AUC close to 0.99, high precision and recall, and a stable confusion matrix. These results demonstrate that lightweight architectures, when combined with appropriate training strategies and careful handling of class imbalance, can outperform more complex models. Overall, the study confirms that architectural efficiency and learning paradigm selection are more critical than model depth alone, making lightweight models well-suited to practical, real-time video anomaly detection scenarios.
First published online 02 February 2026
Keywords:
video anomaly detection; lightweight models; convolutional autoencoder; MobileNetV2; frame-level detection;avenue dataset.How to Cite
Share
License
Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University.
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Abdalla, M., Javed, S., Radi, M. Al, Ulhaq, A., & Werghi, N. (2024). Video anomaly detection in 10 years: A survey and Outlook. arXiv. https://doi.org/10.48550/arXiv.2405.19387
Adam, A., Rivlin, E., Shimshoni, I., & Reinitz, D. (2008). Robust real-time unusual event detection using multiple fixed-location monitors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(3), 555–560. https://doi.org/10.1109/TPAMI.2007.70825
Anoopa, S., & Salim, A. (2022). Survey on anomaly detection in surveillance videos. Materials Today: Proceedings, 58, 162–167. https://doi.org/10.1016/j.matpr.2022.01.171
Barbalau, A., Ionescu, R. T., Georgescu, M.-I., Dueholm, J., Ramachandra, B., Nasrollahi, K., Khan, F. S., Moeslund, T. B., & Shah, M. (2023). SSMTL++: Revisiting self-supervised multi-task learning for video anomaly detection. arXiv. https://doi.org/10.48550/arXiv.2207.08003
Choudhry, N., Abawajy, J., Huda, S., & Rao, I. (2023). A comprehensive survey of machine learning methods for surveillance videos anomaly detection. IEEE Access, 11, 114680–114713. https://doi.org/10.1109/ACCESS.2023.3321800
Cong, Y., Yuan, J., & Liu, J. (2011). Sparse reconstruction cost for abnormal event detection. In CVPR 2011 (pp. 3449–3456). IEEE. https://doi.org/10.1109/CVPR.2011.5995434
Davis, J., & Goadrich, M. (2006). The relationship between Precision-Recall and ROC curves. In Proceedings of the 23rd International Conference on Machine Learning – ICML ’0, (pp. 233–240). Association for Computing Machinery. https://doi.org/10.1145/1143844.1143874
Doshi, K., & Yilmaz, Y. (2020). Continual learning for anomaly detection in surveillance videos. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (pp. 1025–1034). IEEE. https://doi.org/10.1109/CVPRW50498.2020.00135
Duong, H.-T., Le, V.-T., & Hoang, V. T. (2023). Deep learning-based anomaly detection in video surveillance: A survey. Sensors, 23(11), Article 5024. https://doi.org/10.3390/s23115024
Fernandes, G., Rodrigues, J. J. P. C., Carvalho, L. F., Al-Muhtadi, J. F., & Proença, M. L. (2019). A comprehensive survey on network anomaly detection. Telecommunication Systems, 70(3), 447–489. https://doi.org/10.1007/s11235-018-0475-8
Glegoła, W., Karpus, A., & Przybyłek, A. (2021). MobileNet family tailored for Raspberry Pi. Procedia Computer Science, 192, 2249–2258. https://doi.org/10.1016/j.procs.2021.08.238
Gnouma, M., Ejbali, R., & Zaied, M. (2018). Abnormal events’ detection in crowded scenes. Multimedia Tools and Applications, 77(19), 24843–24864. https://doi.org/10.1007/s11042-018-5701-6
Gong, D., Liu, L., Le, V., Saha, B., Mansour, M. R., Venkatesh, S., & Van Den Hengel, A. (2019). Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection. In 2019 IEEE/CVF International Conference on Computer Vision (ICCV) (pp. 1705–1714). IEEE. https://doi.org/10.1109/ICCV.2019.00179
Hasan, M., Choi, J., Neumann, J., Roy-Chowdhury, A. K., & Davis, L. S. (2016). Learning temporal regularity in video sequences. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 733–742). IEEE. https://doi.org/10.1109/CVPR.2016.86
Johnson, J. M., & Khoshgoftaar, T. M. (2019). Survey on deep learning with class imbalance. Journal of Big Data, 6(1), Article 27. https://doi.org/10.1186/s40537-019-0192-5
Keleko Teguede, A. (2022). Deep Learning for anomaly detection in industry 4.0 [Doctoral thesis]. Institut National Polytechnique de Toulouse. https://theses.hal.science/tel-04248257v1
Li, Z., Yan, Y., Wang, X., Ge, Y., & Meng, L. (2025). A survey of deep learning for industrial visual anomaly detection. Artificial Intelligence Review, 58(9), Article 279. https://doi.org/10.1007/s10462-025-11287-7
Liu, W., Luo, W., Lian, D., & Gao, S. (2018). Future frame prediction for anomaly detection – a new baseline. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 6536–6545). IEEE. https://doi.org/10.1109/CVPR.2018.00684
Mahadevan, V., Li, W., Bhalodia, V., & Vasconcelos, N. (2010). Anomaly detection in crowded scenes. In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 1975–1981). IEEE. https://doi.org/10.1109/CVPR.2010.5539872
Medel, J. R., & Savakis, A. (2016). Anomaly detection in video using predictive convolutional long short-term memory networks. arXiv. https://doi.org/10.48550/arXiv.1612.0039
Middha K, Goyal S, Malhotra A, & Jain N. (2024). Anomaly detection in CCTV surveillance. International Journal for Multidisciplinary Research, 6(1). https://doi.org/10.36948/ijfmr.2024.v06i01.12750
Noghre, G. A., Pazho, A. D., & Tabkhi, H. (2025). A survey on video anomaly detection via deep learning: Human, vehicle, and environment. arXiv. https://doi.org/10.48550/arXiv.2508.14203
Pang, G., Shen, C., Cao, L., & Hengel, A. Van Den. (2022). Deep learning for anomaly detection. ACM Computing Surveys, 54(2), 1–38. https://doi.org/10.1145/3439950
Pathirannahalage, I., Jayasooriya, V., Samarabandu, J., & Subasinghe, A. (2024). A comprehensive analysis of real-time video anomaly detection methods for human and vehicular movement. Multimedia Tools and Applications, 84(10), 7519–7564. https://doi.org/10.1007/s11042-024-19204-w
Patrikar, D. R., & Parate, M. R. (2022). Anomaly detection using edge computing in video surveillance system: review. International Journal of Multimedia Information Retrieval, 11(2), 85–110. https://doi.org/10.1007/s13735-022-00227-8
Ristea, N. C., Madan, N., Ionescu, R. T., Nasrollahi, K., Khan, F. S., Moeslund, T. B., & Shah, M. (2022). Self-supervised predictive convolutional attentive block for anomaly detection. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 13566–13576). IEEE. https://doi.org/10.1109/CVPR52688.2022.01321
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L.-C. (2018). MobileNetV2: Inverted residuals and linear bottlenecks. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 4510–4520). IEEE. https://doi.org/10.1109/CVPR.2018.00474
Suba, N., Verma, A., Baviskar, P., & Varma, S. (2022). Violence detection for surveillance systems using lightweight CNN models. In 7th International Conference on Computing in Engineering & Technology (ICCET 2022) (pp. 23–29). IET. https://doi.org/10.1049/icp.2022.0587
Sultani, W., Chen, C., & Shah, M. (2019). Real-world anomaly detection in surveillance videos. arXiv. https://doi.org/10.48550/arXiv.1801.04264
Wu, P., Pan, C., Yan, Y., Pang, G., Wang, P., & Zhang, Y. (2024). Deep learning for video anomaly detection: A review. arXiv. https://doi.org/10.48550/arXiv.2409.05383
Yadav, R. K., & Kumar, R. (2022). A survey on video anomaly detection. In 2022 IEEE Delhi Section Conference (DELCON) (pp. 1–5). https://doi.org/10.1109/DELCON54057.2022.9753580
Zhao, Y., Deng, B., Shen, C., Liu, Y., Lu, H., & Hua, X. S. (2017). Spatio-temporal AutoEncoder for video anomaly detection. In MM 2017 – Proceedings of the 2017 ACM Multimedia Conference (pp. 1933–1941). Association for Computing Machinery. https://doi.org/10.1145/3123266.3123451
View article in other formats
CrossMark check
Published
Issue
Section
Copyright
Copyright (c) 2025 The Author(s). Published by Vilnius Gediminas Technical University.
License
This work is licensed under a Creative Commons Attribution 4.0 International License.