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Optimized Object Detection Technique in Video Surveillance System Using Depth Images

  • Md. Shahzad AlamEmail author
  • T. S. Ashwin
  • G. Ram Mohana Reddy
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 766)

Abstract

In real-time surveillance and intrusion detection, it is difficult to rely only on RGB image-based videos as the accuracy of detected object is low in the low light condition and if the video surveillance area is completely dark then the object will not be detected. Hence, in this paper, we propose a method which can increase the accuracy of object detection even in low light conditions. This paper also shows how the light intensity affects the probability of object detection in RGB, depth, and infrared images. The depth information is obtained from Kinect sensor and YOLO architecture is used to detect the object in real-time. We experimented the proposed method using real-time surveillance system which gave very promising results when applied on depth images which were taken in low light conditions. Further, in real-time object detection, we cannot apply object detection technique before applying any image preprocessing. So we investigated the depth image by which the accuracy of object detection can be improved without applying any image preprocessing. Experimental results demonstrated that depth image (96%) outperforms RGB image (48%) and infrared image (54%) in extreme low light conditions.

Keywords

Depth image Object detection Kinect Real-time video surveillance 

Notes

Acknowledgements

Authors have obtained all ethical approvals from the Institutional Ethics Committee (IEC) of National Institute of Technology Karnataka Surathkal, Mangalore, India and a written consent was also obtained from the human subject.

References

  1. 1.
    Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection (2015). arXiv:1506.02640
  2. 2.
    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
  3. 3.
    Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)Google Scholar
  4. 4.
    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
  5. 5.
    Southwell, B.J., Fang, G.: Human object recognition using color and depth information from an RGB-D Kinect sensor. Int. J. Adv. Robot. Syst. 10, 171 (2013)CrossRefGoogle Scholar
  6. 6.
    Manap, M.S.A., Sahak, R., Zabidi, A., Yassin, I., Tahir, N.M.: Object detection using depth information from Kinect sensor. In: 2015 IEEE 11th International Colloquium on Signal ProcessingGoogle Scholar
  7. 7.
    Hou, S., Wang, Z., Wu, F.: Deeply exploit depth information for object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)Google Scholar
  8. 8.
    Cao, Y., Shen, C., Shen, H.T.: Exploiting depth from single monocular images for object detection and semantic segmentation. IEEE Trans. Image Process. 26(2) (2017)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Pham, T.T.D., Nguyen, H.T., Lee, S., Won, C.S.: Moving object detection with Kinect v2. In: 2016 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Md. Shahzad Alam
    • 1
    Email author
  • T. S. Ashwin
    • 1
  • G. Ram Mohana Reddy
    • 1
  1. 1.National Institute of Technology KarnatakaSurathkalIndia

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