Realtime Object Detection using OpenCV


Realtime Object Detection using OpenCV

  • In this project we will deeper and look at various algorithms that can be used for object detection and counting for industrial applications
  • We will start with the algorithms belonging to RCNN family, i.e. RCNN, Fast RCNN and Faster RCNN.
  • In the upcoming article of this series, we will cover more advanced algorithms like YOLO, SSD, etc.

The algorithm applies a neural network to an entire image. The network divides the image into an S x S grid and comes up with bounding boxes, which are boxes drawn around images and predicted probabilities for each of these regions.The method used to come up with these probabilities is logistic regression. The bounding boxes are weighted by the associated probabilities. For class prediction, independent logistic classifiers are used.In this article, demonstrate how to implement the YOLO algorithm with a pre trained model.First, we would need to install DarkNet. it is a neural network framework that is open source.Realtime Object Detection using OpenCV

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Existing Method:

  • Edge detection.
  • Morphological filters.
  • Svm classification.


  • Not a real-time application.
  • Information about objects is very little.

Proposed Method:

  • Caffe model data set(Darknet)
  • Deep learning classification
  • Blob detection


  • Maximum accuracy in classification
  • Real-time achievement
  • Machine-based prediction


  • Commercial applications
  • Forensic lab
  • Face recognition


  • Python idle.
  • Open CV modules.


  • Windows OS PC.
  • Minimum 2GB RAM.


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