Real Time Object Detection in Surveillance Cameras with Distance Estimation Using Parallel Implementation

EasyChair Preprint 3338

Abstract

Object detection is not only shaping how computers see and analyze things but it is also helping in the behavior of how an object reacts to the change in its environment. The main application of these object detection sensors or software is to find the location of an object in space or to track its movement. Object detection has infinitely many use cases and in this paper, we are introducing an application that will allow the safety of users struck in a disaster and who need to be evacuated. In such cases, the main thing to focus on and to eradicate is camera noise, saturation and image compression. Our solution is to establish a connection between the person struck in a disaster with fire safety people. This works over a convolutional network that allows us to detect vulnerable things present inside a room that needs to be rescued and can also give an insight into any explosive inside the room. Our model uses Faster-RCNN and COCO which is a pretrained dataset. This allows real-time object detection and classification on our network. Using this we were able to detect an object or a person and get him to rescue by providing them the shortest way out of that place. With this, we were able to get an accuracy of more than 75% in our object detection model.

Keyphrases: Convolutional Neural Networks, Faster RCNN, computer vision, object detection

@booklet{EasyChair:3338,
  author    = {Meherdeep Thakur and Saira J. Banu},
  title     = {Real Time Object Detection in Surveillance Cameras with Distance Estimation Using Parallel Implementation},
  howpublished = {EasyChair Preprint 3338},
  year      = {EasyChair, 2020}}