ChangeNet: A Deep Learning Architecture for Visual Change Detection

  • Ashley Varghese
  • Jayavardhana Gubbi
  • Akshaya RamaswamyEmail author
  • P. Balamuralidhar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11130)


The increasing urban population in cities necessitates the need for the development of smart cities that can offer better services to its citizens. Drone technology plays a crucial role in the smart city environment and is already involved in a number of functions in smart cities such as traffic control and construction monitoring. A major challenge in fast growing cities is the encroachment of public spaces. A robotic solution using visual change detection can be used for such purposes. For the detection of encroachment, a drone can monitor outdoor urban areas over a period of time to infer the visual changes. Visual change detection is a higher level inference task that aims at accurately identifying variations between a reference image (historical) and a new test image depicting the current scenario. In case of images, the challenges are complex considering the variations caused by environmental conditions that are actually unchanged events. Human mind interprets the change by comparing the current status with historical data at intelligence level rather than using only visual information. In this paper, we present a deep architecture called ChangeNet for detecting changes between pairs of images and express the same semantically (label the change). A parallel deep convolutional neural network (CNN) architecture for localizing and identifying the changes between image pair has been proposed in this paper. The architecture is evaluated with VL-CMU-CD street view change detection, TSUNAMI and Google Street View (GSV) datasets that resemble drone captured images. The performance of the model for different lighting and seasonal conditions are experimented quantitatively and qualitatively. The result shows that ChangeNet outperforms the state of the art by achieving 98.3% pixel accuracy, 77.35% object based Intersection over Union (IoU) and 88.9% area under Receiver Operating Characteristics (RoC) curve.


Change detection CNN 


  1. 1.
    Sahi, K.M., Wheelock, C.: Drones for commercial applications. Tractica Research Report (2017)Google Scholar
  2. 2.
    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)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Hussain, M., Chen, D., Cheng, A., Wei, H., Stanley, D.: Change detection from remotely sensed images: from pixel-based to object-based approaches. ISPRS J. Photogram. Remote Sens. 80, 91–106 (2013)CrossRefGoogle Scholar
  4. 4.
    Alcantarilla, P.F., Stent, S., Ros, G., Arroyo, R., Gherardi, R.: Street-view change detection with deconvolutional networks. Auton. Robots 42, 1301–1322 (2016). Robotics: Science and SystemsCrossRefGoogle Scholar
  5. 5.
    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)Google Scholar
  6. 6.
    Sakurada, K., Okatani, T.: Change detection from a street image pair using CNN features and superpixel segmentation. In: BMVC, p. 61-1 (2015)Google Scholar
  7. 7.
    Rensink, R.A.: Change detection. Annu. Rev. Psychol. 53(1), 245–277 (2002)CrossRefGoogle Scholar
  8. 8.
    Goyette, N., Jodoin, P.M., Porikli, F., Konrad, J., Ishwar, P.: a new change detection benchmark dataset. In: 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 1–8, June 2012Google Scholar
  9. 9.
    Wang, Y., Jodoin, P.M., Porikli, F., Konrad, J., Benezeth, Y., Ishwar, P.: CDnet 2014: an expanded change detection benchmark dataset. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 393–400, June 2014Google Scholar
  10. 10.
    Bilodeau, G.A., Jodoin, J.P., Saunier, N.: Change detection in feature space using local binary similarity patterns. In: 2013 International Conference on Computer and Robot Vision, CRV, pp. 106–112. IEEE (2013)Google Scholar
  11. 11.
    Sedky, M., Moniri, M., Chibelushi, C.C.: Spectral-360: a physics-based technique for change detection. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June 2014Google Scholar
  12. 12.
    De Gregorio, M., Giordano, M.: Change detection with weightless neural networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June 2014Google Scholar
  13. 13.
    Wang, R., Bunyak, F., Seetharaman, G., Palaniappan, K.: Static and moving object detection using flux tensor with split Gaussian models. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 420–424 (2014)Google Scholar
  14. 14.
    Bianco, S., Ciocca, G., Schettini, R.: How far can you get by combining change detection algorithms? CoRR abs/1505.02921 (2015)Google Scholar
  15. 15.
    Gressin, A., Vincent, N., Mallet, C., Paparoditis, N.: Semantic approach in image change detection. In: Blanc-Talon, J., Kasinski, A., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2013. LNCS, vol. 8192, pp. 450–459. Springer, Cham (2013). Scholar
  16. 16.
    Kataoka, H., Shirakabe, S., Miyashita, Y., Nakamura, A., Iwata, K., Satoh, Y.: Semantic change detection with hypermaps. arXiv preprint arXiv:1604.07513 (2016)
  17. 17.
    Gubbi, J., Ramaswamy, A., Sandeep, N.K., Varghese, A., Balamuralidhar, P.: Visual change detection using multiscale super pixel. In: Digital Image Computing: Techniques and Applications (2017)Google Scholar
  18. 18.
    Bansal, A., Russell, B.C., Gupta, A.: Marr revisited: 2D-3D alignment via surface normal prediction. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, 27–30 June 2016, pp. 5965–5974 (2016)Google Scholar
  19. 19.
    Bansal, A., Chen, X., Russell, B.C., Gupta, A., Ramanan, D.: PixelNet: representation of the pixels, by the pixels, and for the pixels. CoRR abs/1702.06506 (2017)Google Scholar
  20. 20.
    Du, W., Fang, M., Shen, M.: Siamese convolutional neural networks for authorship verificationGoogle Scholar
  21. 21.
    Mueller, J., Thyagarajan, A.: Siamese recurrent architectures for learning sentence similarity. In: AAAI, pp. 2786–2792 (2016)Google Scholar
  22. 22.
    Koch, G.: Siamese neural networks for one-shot image recognition. Master’s thesis. University of Toronto, Canada (2015)Google Scholar
  23. 23.
    Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)Google Scholar
  24. 24.
    Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)Google Scholar
  25. 25.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  26. 26.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
  27. 27.
    Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). Scholar
  28. 28.
    Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. arXiv preprint arXiv:1612.01105 (2016)
  29. 29.
    Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. arXiv preprint arXiv:1511.00561 (2015)

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ashley Varghese
    • 1
  • Jayavardhana Gubbi
    • 1
  • Akshaya Ramaswamy
    • 1
    Email author
  • P. Balamuralidhar
    • 1
  1. 1.Embedding Systems and Robotics, TCS Research and InnovationBengaluruIndia

Personalised recommendations