This is the last article of the blog series for Object Detection with R-CNN.

If you are reading this blog, congratulations for getting this far. Now you are ready to experiment the performance of your RCNN classifier. I will use my own image to see whether the classifier can detect my face.

This is part of the blog series for Object Detection with R-CNN.

Cited from VGG in TensorFlow.

In this blog, we are now ready to train our classifier for object detection. We will use a large pre-trained CNN to extract a fixed-length feature vector from each region, and then create artificial neural networks that mapps the feature vector to the object class. We will focus on detecting a person.

This is part of the blog series for Object Detection with R-CNN.

In this blog, we will review the selective sarch algorithm. The selective search is one of the most successful category-independent region proposal algorithms, and R-CNN also uses selective search to find region proposal.

J.R.R. Uijlings et al take a hierarchical grouping algorithm to form the basis of selective search, and first apply fast segmentation method of Felzenszwalb and Huttenlocher

Cited from Rich feature hierarchies for accurate object detection and semantic segmentation paper

This is the second blog post of "Object Detection with R-CNN" series.

In this blog, I will review Rich feature hierarchies for accurate object detection and semantic segmentation paper to understand Regions with CNN features (R-CNN) method. R-CNN is a successful object detection algorithm that can return class label of objects and their bounding boxes for a given image. The work is published in 2013 and there have been many faster algorithms for the object detection algorithm (e.g., fast R-CNN, faster R-CNN and Yolo). But nevertheless, the implementation of the R-CNN is simple, and serves as a powerful bench mark for various object detection tasks. So for that reason, this blog will review the R-CNN algorithm.

This is the first blog post of "Object Detection with R-CNN" series.

Object detection¶

According to the wikipedia:

Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos.