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)


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.


Depth image Object detection Kinect Real-time video surveillance 



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.


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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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