References
- J. Redmon and A. Farhadi, Yolov3: An incremental improvement, arXiv preprint arXiv:1804.02767, 2018.
- R. Grycuk, R. Scherer, A. Marchlewska, and C. Napoli, Semantic hashing for fast solar magnetogram retrieval, Journal of Artificial Intelligence and Soft Computing Research,vol. 12, 2022.
- S. Ren, K. He, R. Girshick, and J. Sun, Faster rcnn: Towards real-time object detection with region proposal networks, Advances in neural information processing systems, vol. 28, 2015.
- W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, Ssd: Single shot multibox detector, in European conference on computer vision. Springer, 2016,pp. 21–37.
- K. Muchtar, A. Bahri, M. Fitria, T. W. Cenggoro, B. Pardamean, A. Mahendra, M. R. Munggaran, and C.-Y. Lin, Moving pedestrian localization and detection with guided filtering, IEEE Access, vol. 10, pp. 89 181–89 196, 2022.
- M.-I. Georgescu, A. Barbalau, R. T. Ionescu, F. S. Khan, M. Popescu, and M. Shah, Anomaly detection in video via self-supervised and multi-task learning, in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2021, pp. 12 742–12 752.
- F. R. Valverde, J. V. Hurtado, and A. Valada, There is more than meets the eye: Self-supervised multi-object detection and tracking with sound by distilling multimodal knowledge, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 11 612–11 621.
- C. Stauffer and W. E. L. Grimson, Adaptive background mixture models for real-time tracking, in Proceedings. 1999 IEEE computer society conference on computer vision and pattern recognition (Cat. No PR00149), vol. 2. IEEE, 1999, pp. 246–252
- O. Barnich and M. Van Droogenbroeck, Vibe: a powerful random technique to estimate the background in video sequences, in 2009 IEEE international conference on acoustics, speech and signal processing. IEEE, 2009, pp. 945–948.
- Z. Qu, S. Yu, and M. Fu, Motion background modeling based on context-encoder, in 2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR). IEEE, 2016, pp. 1–5.
- M. Sultana, A. Mahmood, S. Javed, and S. K. Jung, Unsupervised deep context prediction for background estimation and foreground segmentation, Machine Vision and Applications, vol. 30, no. 3, pp. 375–395, 2019.
- Y. Tao, P. Palasek, Z. Ling, and I. Patras, Background modelling based on generative unet, in 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, 2017, pp. 1–6.
- M. Babaee, D. T. Dinh, and G. Rigoll, A deep convolutional neural network for video sequence background subtraction, Pattern Recognition, vol. 76, pp. 635–649, 2018.
- M. Braham and M. Van Droogenbroeck, Deep background subtraction with scene-specific convolutional neural networks, in 2016 international conference on systems, signals and image processing (IWSSIP). IEEE, 2016, pp. 1–4.
- Y. Wang, Z. Luo, and P.-M. Jodoin, Interactive deep learning method for segmenting moving objects, Pattern Recognition Letters, vol. 96, pp. 66–75, 2017.
- Y. Chen, J. Wang, B. Zhu, M. Tang, and H. Lu, Pixelwise deep sequence learning for moving object detection, IEEE Transactions on Circuits and Systems for Video Technology, vol. 29, no. 9, pp. 2567–2579, 2017.
- Z. Hu, T. Turki, N. Phan, and J. T. Wang, A 3d atrous convolutional long short-term memory network for background subtraction, IEEE Access, vol. 6, pp. 43 450–43 459, 2018.
- D. Sakkos, H. Liu, J. Han, and L. Shao, Endto-end video background subtraction with 3d convolutional neural networks, Multimedia Tools and Applications, vol. 77, no. 17, pp. 23 023–23 041, 2018.
- B. N. Subudhi, M. K. Panda, T. Veerakumar, V. Jakhetiya, and S. Esakkirajan, Kernel-induced possibilistic fuzzy associate background subtraction for video scene, IEEE Transactions on Computational Social Systems, 2022.
- C. Zhao, K. Hu, and A. Basu, Universal background subtraction based on arithmetic distribution neural network, IEEE Transactions on Image Processing, vol. 31, pp. 2934–2949,2022.
- K. He, X. Zhang, S. Ren, and J. Sun, Deep residual learning for image recognition, in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770–778.
- N. Goyette, P.-M. Jodoin, F. Porikli, J. Konrad, and P. Ishwar, Changedetection. net: A new change detection benchmark dataset, in 2012 IEEE computer society conference on computer vision and pattern recognition workshops. IEEE, 2012, pp. 1–8.
- T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, Microsoft coco: Common objects in context, in European conference on computer vision. Springer, 2014, pp. 740–755.