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Multiple object detection using HOG+SVM?

asked 2017-04-30 04:29:45 -0500

John Strood gravatar image

I read a lot of posts on the web that said it was impossible to detect objects of different types like human face, car, dog,cat, banana etc using a single classifier. But I began wondering can we not train a classifier with images of different objects in a multiclass SVM? The SVM predict then returns the class label from which we can infer the type of object. I know this is really simple task by using a Caffe's RCNN. But I wished to know if the method I described was feasible.

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answered 2017-04-30 06:08:15 -0500

berak gravatar image

updated 2017-04-30 06:13:06 -0500

  • using opencv's HOGDescriptor, you can only detect objects of a single class. (it's using a single, binary regression support vector in this case)

  • but you can also compute your own HOG features, and use those with multi-class SVM for classification.

if you're using an SVM, you simply cannot to both at the same time, with the same setup. (it's a bit different with rcnn or yolo, where you get both segmentation and identification)

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Yes, I was indeed talking about the second possibility. That is compute the HOG feature vector and perhaps pair it with LibSVM. But no one's ever talked about it. So I was thinking if it were ever feasible.

John Strood gravatar imageJohn Strood ( 2017-04-30 13:13:06 -0500 )edit
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Asked: 2017-04-30 04:29:45 -0500

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Last updated: Apr 30 '17