Deep Learning Models for Analysis of Traffic and Crowd Management from Surveillance Videos

  • S. SeemaEmail author
  • Suhas Goutham
  • Smaranita Vasudev
  • Rakshith R. Putane
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1119)


Deep learning models have been used in the field of object detection and object counting. The problem statement dealt with in this paper aims to achieve the objectives of traffic and crowd management. The Single Shot MultiBox Detector (SSD) model is used in conjunction with a line of counting approach to count the objects of interest in a video captured using surveillance cameras. The proposed model has been used for analyzing traffic surveillance videos to make intelligent traffic decisions to prioritize traffic signals based on the traffic densities. As a sub case of traffic management, a Tesseract OCR model is used to capture the license plate of vehicles violating any traffic regulations. For crowd management, surveillance videos are analyzed to obtain the crowd statistics to handle crowd management in cases of emergencies and huge public gatherings for safety and security.


Deep learning Neural networks Single Shot MultiBox Detector Line of interest counting Tesseract Optical character recognition Surveillance videos 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • S. Seema
    • 1
    Email author
  • Suhas Goutham
    • 1
  • Smaranita Vasudev
    • 1
  • Rakshith R. Putane
    • 1
  1. 1.Department of Computer Science and EngineeringM.S. Ramaiah Institute of TechnologyBangaluruIndia

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