Uma Abordagem baseada em Redes Neurais, Multiple Instance Learning e PCA para Detecção de Anomalias em Videovigilância
Resumo
Multiple Instance Learning (MIL) tem se tornado uma solução atrativa na literatura de videovigilância por permitir lidar com bases fracamente rotuladas. Este trabalho propõe e avalia uma abordagem para detecção de anomalias em vídeo baseada em classificação binária com redes neurais Multilayer Perceptron (MLP) e paradigma MIL. Os experimentos foram conduzidos a partir de um conjunto de atributos I3D (Inflated 3D) referentes ao dataset de benchmark ShanghaiTech. Explora-se ainda o efeito da compacticidade dos dados e representação de informação essencial com a técnica de extração de atributos Principal Component Analysis (PCA). Os resultados alcançados foram competitivos quando comparados com o estado da arte.
Palavras-chave:
detecção de anomalia, videovigilância, Multiple Instance Learning, atributos I3D, Multilayer Perceptron
Referências
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Ribeiro, M., Lazzaretti, A. E., and Lopes, H. S. (2018). A study of deep convolutional auto-encoders for anomaly detection in videos. Pattern Recognition Letters, 105:13–22.
Saha, B. N., Ray, N., and Zhang, H. (2009). Snake validation: A pca-based outlier detection method. IEEE Signal Processing Letters, 16(6):549–552.
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Sultani, W., Chen, C., and Shah, M. (2018). Real-world anomaly detection in surveillance videos. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 6479–6488.
Wan, B., Fang, Y., Xia, X., and Mei, J. (2020). Weakly supervised video anomaly detection via center-guided discriminative learning. In 2020 IEEE International Conference on Multimedia and Expo (ICME), pages 1–6. IEEE.
Yun, K., Honorio, J., Chattopadhyay, D., Berg, T. L., and Samaras, D. (2012). Two-person interaction detection using body-pose features and multiple instance learning. In 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pages 28–35. IEEE.
Zhao, H., Lai, Z., Leung, H., and Zhang, X. (2020). Feature Learning and Understanding: Algorithms and Applications. Springer Nature.
Haykin, S. (2010). Neural networks and learning machines, 3/E. Pearson Education India.
Kamoona, A. M., Gosta, A. K., Bab-Hadiashar, A., and Hoseinnezhad, R. (2020). Multiple instance-based video anomaly detection using deep temporal encoding-decoding. arXiv preprint arXiv:2007.01548.
Kingma, D. P. and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
Kuhn, M., Johnson, K., et al. (2013). Applied predictive modeling, volume 26. Springer.
Li, T., Wang, Z., Liu, S., and Lin, W.-Y. (2021). Deep unsupervised anomaly detection. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 3636–3645.
Nayak, R., Pati, U. C., and Das, S. K. (2020). A comprehensive review on deep learning-based methods for video anomaly detection. Image and Vision Computing, page 104078.
Pawar, K. and Attar, V. (2019). Deep learning approaches for video-based anomalous activity detection. World Wide Web, 22(2):571–601.
Pereira, S. S. L. and Maia, J. E. (2021). Anomaly detection in surveillance video of natural environment. International Journal of Computer Applications, 183(1):1–7.
Rao, T. N., Girish, G., and Rajan, J. (2017). An improved contextual information based approach for anomaly detection via adaptive inference for surveillance application. In Proceedings of International Conference on Computer Vision and Image Processing, pages 133–147. Springer.
Ribeiro, M., Lazzaretti, A. E., and Lopes, H. S. (2018). A study of deep convolutional auto-encoders for anomaly detection in videos. Pattern Recognition Letters, 105:13–22.
Saha, B. N., Ray, N., and Zhang, H. (2009). Snake validation: A pca-based outlier detection method. IEEE Signal Processing Letters, 16(6):549–552.
Suarez, J. J. P. and Naval Jr, P. C. (2020). A survey on deep learning techniques for video anomaly detection. arXiv preprint arXiv:2009.14146.
Sultani, W., Chen, C., and Shah, M. (2018). Real-world anomaly detection in surveillance videos. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 6479–6488.
Wan, B., Fang, Y., Xia, X., and Mei, J. (2020). Weakly supervised video anomaly detection via center-guided discriminative learning. In 2020 IEEE International Conference on Multimedia and Expo (ICME), pages 1–6. IEEE.
Yun, K., Honorio, J., Chattopadhyay, D., Berg, T. L., and Samaras, D. (2012). Two-person interaction detection using body-pose features and multiple instance learning. In 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pages 28–35. IEEE.
Zhao, H., Lai, Z., Leung, H., and Zhang, X. (2020). Feature Learning and Understanding: Algorithms and Applications. Springer Nature.
Publicado
18/07/2021
Como Citar
PEREIRA, Silas S. L.; MAIA, J. E. Bessa.
Uma Abordagem baseada em Redes Neurais, Multiple Instance Learning e PCA para Detecção de Anomalias em Videovigilância. In: SEMINÁRIO INTEGRADO DE SOFTWARE E HARDWARE (SEMISH), 48. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 123-130.
ISSN 2595-6205.
DOI: https://doi.org/10.5753/semish.2021.15814.
