CODEWITHSUNDEEP
c++opencvimage-processingcomputer-visionobject-detection
PeakGen
PeakGen

Reputation: 22995

Object Detection: Training Requried or No Training Required?

This question is related to Object detection, and basically, detecting any "known" object. For an example, imagine I have the below objects.

  1. Table
  2. Bottle.
  3. Camera
  4. Car

I will take 4 photos from all of these individual object. One from left, another from right, and other 2 from up and down. I originally thought it is possible to recognize these objects with these 4 photos per each, because you have the photos in all 4 angles, no matter how you see the object you can detect it.

But I got confused with someones idea about training the engine with thousands of positive and negative images from each object. I really don't think this is required.

So simply speaking, my question is, in order to identify an object, do I need these thousands of positive and negative objects? Or else simply 4 photos from 4 angles is enough?

I am expecting to use OpenCV for this.

Update

Actually the main thing is something like this.. Imagine that I have 2 laptops. One is Dell and the other one is HP. Both are laptops but you know, they have clearly visible differences including the Logo. Can we do this using Feature Description? If not, how "hard" the "training" process? How many pics needed?

Update 2 I need to detect "specific" objects. Not all the cars, all the bottles etc. For an example, the "Maruti Car Model 123" and "Ferrari Car Model 234" are both cars but different. Imagine I have the pictures of Maruti and Ferrari of above mentioned models, then I need to detect them. I don't have to worry about other cars or vehicles, or even other models of Maruti and Ferrari. But the above mentioned "Maruti Car Model 123" should be identified as "Maruti Car Model 123" and above mentioned "Ferrari Car Model 234"should be identified as "Ferrari Car Model 234". How many pictures do I need for this?

Upvotes: 0

Views: 1258

Answers (2)

GilLevi
GilLevi

Reputation: 2137

Answers:

  1. If you want to detect a specific object and you don't need to account for view point changes, you can use 2D features: http://docs.opencv.org/doc/tutorials/features2d/feature_homography/feature_homography.html

  2. To distinguish between 2 logos, you'll probably need to build a detector for each logo which will be trained on a set of images. For example, you can train a Haar cascade classifier.

  3. To distinguish between different models of cars, you'll probably need to train a classifier using training images of each car. However, I encountered an application which does that using a nearest neighbor approach - it just extracts features from the given test image and compares it to a know set of images of difference car models.

Also, I can recommend some approaches and packages if you'll explain more on the application.

Upvotes: 1

cangrejo
cangrejo

Reputation: 2202

To answer the question you asked in the title, if you want to be able to determine what the object in the picture is you need a supervised algorithm (a.k.a. trained). Otherwise you would be able to determine, in some cases, the edges or the presence of an object, but not what kind of an object it is. In order to tell what the object is you need a labelled training set.

Regarding the contents of the question, the number of possible angles in a picture of an object is infinite. If you just have four pictures in your training set, the test example could be taken in an angle that falls halfway between training example A and training example B, making it hard to recognize for your algorithm. The larger the training set the higher the probability of recognizing the object. Be careful: you never reach the absolute certainty that your algorithm will recognize the object. It just becomes more likely.

Upvotes: 1

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