Abstract
Background subtraction is commonly adopted for detecting moving objects in image sequence. It is an important and fundamental computer vision task and has a wide range of applications. We propose a background subtraction framework with deep learning model. Pixels are labeled as background or foreground by an Encoder-Decoder Structured Convolutional Neural Network (CNN). The encoder part produces a high-level feature vector. Then, the decoder part uses the feature vector to generate a binary segmentation map, which can be used to identify moving objects. The background model is generated from the image sequence. Each frame of the image sequence and the background model are input to the CNN for pixel classification. Background subtraction result can be erroneous as videos may be captured in various complex scenes. The background model must be updated. Therefore, we propose a feedback scheme to perform the pixelwise background model updating. For the training of the CNN, the input images and the corresponding ground truths are drawn from the benchmark dataset Change Detection 2014. The results show that our proposed architecture outperforms many well-known traditional and deep learning background subtraction algorithms.
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References
Bouwmans, T.: Background subtraction for visual surveillance: a fuzzy approach. In: Handbook on Soft Computing for Video Surveillance. Taylor and Francis Group (2012)
El Baf, F., Bouwmans, T.: Comparison of background subtraction methods for a multimedia learning space. In: Proceedings of International Conference on Signal Processing and Multimedia (2007)
Hsieh, J.-W., Hsu, Y.-T., Liao, H.-Y.M., Chen, C.-C.: Video-based human movement analysis and its application to surveillance systems. IEEE Trans. Multimedia 10(3), 372–384 (2008)
Lu, W., Tan, Y.-P.: A vision-based approach to early detection of drowning incidents in swimming pools. IEEE Trans. Circuits Syst. Video Technol. 14(2), 159–178 (2004)
Visser, R., Sebe, N., Bakker, E.: Object recognition for video retrieval. In: Proceedings of International Conference on Image and Video Retrieval, pp. 250–259 (2002)
Elhabian, S.Y., El-Sayed, K.M., Ahmed, S.H.: Moving object detection in spatial domain using background removal techniques – state-of-art. Recent Pat. Comput. Sci. 1, 32–54 (2008)
Bouwmans, T.: Recent advanced statistical background modeling for foreground detection - a systematic survey. Recent Pat. Comput. Sci. 4(3), 147–176 (2011)
Stauffer, C., Grimson, W.E.L.: Learning patterns of activity using real-time tracking. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 747–757 (2000)
Zivkovic, Z.: Improved adaptive Gaussian mixture model for background subtraction. In: Proceedings of International Conference on Pattern Recognition, pp. 28–31 (2004)
Elgammal, A., Duraiswami, R., Harwood, D., Davis, L.S.: Background and foreground modeling using nonparametric kernel density estimation for visual surveillance. Proc. IEEE 90(7), 1151–1163 (2002)
Barnich, O., Van Droogenbroeck, M.: ViBE: a powerful random technique to estimate the background in video sequences. In: Proceedings of International Conference Acoustics, Speech and Signal Processing, pp. 945–948 (2009)
Mahadevan, V., Vasconcelos, N.: Spatiotemporal saliency in dynamic scenes. IEEE Trans. Pattern Anal. Mach. Intell. 32(1), 171–177 (2010)
Heikkilä, M., Pietikäinen, M.: A texture-based method for modeling the background and detecting moving objects. IEEE Trans. Pattern Anal. Mach. Intell. 28(4), 657–662 (2006)
Liao, S., Zhao, G., Kellokumpu, V., Pietikäinen, M., Li, S.Z.: Modeling pixel process with scale invariant local patterns for background subtraction in complex scenes. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1301–1306 (2010)
St-Charles, P.-L., Bilodeau, G.-A., Bergevin, R.: SuBSENSE: a universal change detection method with local adaptive sensitivity. IEEE Trans. Image Process. 24(1), 359–373 (2015)
Wang, Y., Luo, Z., Jodoin, P.-M.: Interactive deep learning method for segmenting moving objects. Pattern Recogn. Lett. 96, 66–75 (2017)
Lim, L.A., Keles, H.Y.: Foreground segmentation using a triplet convolutional neural network for multiscale feature encoding. arXiv:1801.02225 [cs.CV] (2018)
Babaee, M., Dinh, D.T., Rigoll, G.: A deep convolutional neural network for video sequence background subtraction. Pattern Recogn. 76, 635–649 (2018)
Van Droogenbroeck, M., Paquot, O.: Background subtraction: experiments and improvements for ViBE. In: Proceedings of IEEE Workshop on Change Detection at IEEE Conference on Computer Vision and Pattern Recognition, pp. 32–37 (2012)
Kim, S.W., Yun, K., Yi, K.M., Kim, S.J., Choi, J.Y.: Detection of moving objects with a moving camera using non-panoramic background model. Mach. Vis. Appl. 24, 1015–1028 (2013)
Lauguard, B., Piérard, S., Van Droogenbroeck, M.: LaBGen-P: a pixel-level stationary background generation method based on LaBGen. In: Proceedings of IEEE International Conference on Pattern Recognition, pp. 107–113 (2016)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Wang, Y., Jodoin, P.-M., Porikli, F., Konrad, J., Benezeth, Y., Ishwar, P.: CDnet 2014: an expanded change detection benchmark dataset. In: Proceedings of IEEE Workshop on Change Detection at CVPR-2014, pp. 387–394 (2014)
Noh, H., Hong, S., Han, B.: Learning deconvolution network for semantic segmentation. In: Proceedings of International Conference on Computer Vision (2015)
St-Charles, P.-L., Bilodeau, G.-A., Bergevin, R.: A self-adjusting approach to change detection based on background word consensus. In: Proceedings of IEEE Winter Conference on Applications of Computer Vision, pp. 990–997 (2015)
Wang, R., Bunyak, F., Seetharaman, G., Palaniappan, K.: Static and moving object detection using flux tensor with split gaussian models. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops (2014)
Chen, Y., Wang, J., Lu, H.: Learning sharable models for robust background subtraction. In: Proceedings of IEEE International Conference on Multimedia and Expo (2015)
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Wang, J., Chan, K.L. (2020). Background Subtraction Based on Encoder-Decoder Structured CNN. In: Palaiahnakote, S., Sanniti di Baja, G., Wang, L., Yan, W. (eds) Pattern Recognition. ACPR 2019. Lecture Notes in Computer Science(), vol 12047. Springer, Cham. https://doi.org/10.1007/978-3-030-41299-9_27
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