Object Detection for Crime Scene Evidence Analysis Using Deep Learning

  • Surajit SaikiaEmail author
  • E. Fidalgo
  • Enrique Alegre
  • Laura Fernández-Robles
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10485)


Object detection is the key module in most visual-based surveillance applications and security systems. In crime scene analysis, the images and videos play a significant role in providing visual documentation of a scene. It allows police officers to recreate a scene for later analysis by detecting objects related to a specific crime. However, due to the presence of a large volume of data, the task of detecting objects of interest is very tedious for law enforcement agencies. In this work, we present a Faster R-CNN (Region-based Convolutional Neural Network) based real-time system, which automatically detects objects which might be found in an indoor environment. To test the effectiveness of the proposed system, we applied it to a subset of ImageNet containing 12 object classes and Karina dataset. We achieved an average accuracy of 74.33%, and the mean time taken to detect objects per image was 0.12 s in Nvidia-TitanX GPU.


Object detection Convolutional neural network Deep learning Video surveillance Crime scenes Cyber-security 



This research was funded by the framework agreement between the University of León and INCIBE (Spanish National Cybersecurity Institute) under addendum 22. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X GPU used for this research.


  1. 1.
    Vaidehi, K., Subashini, T.: Automatic classification and retrieval of mammographic tissue density using texture features. In: 2015 IEEE 9th International Conference on Intelligent Systems and Control (ISCO), pp. 1–6. IEEE (2015)Google Scholar
  2. 2.
    Nosato, H., Sakanashi, H., Takahashi, E., Murakawa, M.: Method of retrieving multi-scale objects from optical colonoscopy images based on image-recognition techniques. In: 2015 IEEE Biomedical Circuits and Systems Conference (BioCAS), pp. 1–4. IEEE (2015)Google Scholar
  3. 3.
    Li, J., Ye, D.H., Chung, T., Kolsch, M., Wachs, J., Bouman, C.: Multi-target detection and tracking from a single camera in unmanned aerial vehicles (UAVs). In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4992–4997. IEEE (2016)Google Scholar
  4. 4.
    Rao, R.S., Ali, S.T.: A computer vision technique to detect phishing attacks. In: 2015 Fifth International Conference on Communication Systems and Network Technologies (CSNT), pp. 596–601. IEEE (2015)Google Scholar
  5. 5.
    Herrmann, C., Beyerer, J.: Face retrieval on large-scale video data. In: 2015 12th Conference on Computer and Robot Vision (CRV), pp. 192–199. IEEE (2015)Google Scholar
  6. 6.
    Sidhu, R.S., Sharad, M.: Smart surveillance system for detecting interpersonal crime. In: 2016 International Conference on Communication and Signal Processing (ICCSP), pp. 2003–2007. IEEE (2016)Google Scholar
  7. 7.
    Vallet, A., Sakamoto, H.: Convolutional recurrent neural networks for better image understanding. In: 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA), pp. 1–7. IEEE (2016)Google Scholar
  8. 8.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
  9. 9.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)Google Scholar
  10. 10.
    Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). doi: 10.1007/978-3-319-10602-1_48 Google Scholar
  11. 11.
    Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)MathSciNetCrossRefGoogle Scholar
  12. 12.
    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
  13. 13.
    Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)Google Scholar
  14. 14.
    Razavian, A.S., Sullivan, J., Carlsson, S., Maki, A.: Visual instance retrieval with deep convolutional networks. arXiv preprint arXiv:1412.6574 (2014)
  15. 15.
    Babenko, A., Slesarev, A., Chigorin, A., Lempitsky, V.: Neural codes for image retrieval. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 584–599. Springer, Cham (2014). doi: 10.1007/978-3-319-10590-1_38 Google Scholar
  16. 16.
    Fidalgo, E., Alegre, E., González-Castro, V., Fernández-Robles, L.: Compass radius estimation for improved image classification using edge-sift. Neurocomputing 197, 119–135 (2016)CrossRefGoogle Scholar
  17. 17.
    Lee, H., Grosse, R., Ranganath, R., Ng, A.Y.: Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 609–616. ACM (2009)Google Scholar
  18. 18.
    Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)Google Scholar
  19. 19.
    Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The PASCAL visual object classes (VOC) challenge. Int. J. Comput. Vis. 88(2), 303–338 (2010)CrossRefGoogle Scholar
  20. 20.
    He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8691, pp. 346–361. Springer, Cham (2014). doi: 10.1007/978-3-319-10578-9_23 Google Scholar
  21. 21.
    Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)Google Scholar
  22. 22.
    Fernández-Robles, L., Castejón-Limas, M., Alfonso-Cendón, J., Alegre, E.: Evaluation of clustering configurations for object retrieval using SIFT features. In: Ayuso Muñoz, J.L., Yagüe Blanco, J.L., Capuz-Rizo, S.F. (eds.) Project Management and Engineering. LNMIE, pp. 279–291. Springer, Cham (2015). doi: 10.1007/978-3-319-12754-5_21 Google Scholar
  23. 23.
    Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 675–678. ACM (2014)Google Scholar
  24. 24.
    Dong, C., Loy, C.C., He, K., Tang, X.: Learning a deep convolutional network for image super-resolution. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8692, pp. 184–199. Springer, Cham (2014). doi: 10.1007/978-3-319-10593-2_13 Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Surajit Saikia
    • 1
    • 2
    Email author
  • E. Fidalgo
    • 1
    • 2
  • Enrique Alegre
    • 1
    • 2
  • Laura Fernández-Robles
    • 2
    • 3
  1. 1.Department of Electrical, Systems and AutomationUniversity of LeónLeónSpain
  2. 2.INCIBE (Spanish National Cybersecurity Institute)LeónSpain
  3. 3.Department of Mechanical, Informatics and Aerospace EngineeringUniversity of LeónLeónSpain

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