Deep Learning based missing object Detection and Person Identification: An application for Smart CCTV

• R. C. Dharmik ^[2] ; Sushilkumar Chavhan ^[2] ; S. R. Sathe ^[1]
  1. [1] Visvesvaraya National Institute of Technology

     Visvesvaraya National Institute of Technology

     India

     
  2. [2] Yeshwantrao Chavan College of Engineering (India)
• Localización: 3c Tecnología: glosas de innovación aplicadas a la pyme, ISSN-e 2254-4143, Vol. 11, Nº. 2, 2022, págs. 51-57
• Idioma: inglés
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• Resumen

  • Security and protection are the most crucial concerns in today’s quickly developing world. Deep Learning methods and computer vision assist in resolving both problems. One of the computer vision subtasks that allows us to recognise things is object detection. Videos are a source that is taken into account for detection, and image processing technology helps to increase the effectiveness of state-ofthe-art techniques. With all of these technologies, CCTV is recognised as a key element. Using a deep convolutional neural network, we accept CCTV data in real time in this article. The main objective is to make content the centre of things. Using the YOLO technique, we were able to detect the missing item with an improvement of 10% sparsity over the current state-of-the-art algorithm in the context of surveillance systems, where object detection is a crucial step. It can be utilised to take immediate additional action.