CODEWITHSUNDEEP
opencvsvmdetection
foresea
foresea

Reputation: 73

Does SVM need to do learning each time when detecting people?

I'm using SVM and HOG in OpenCV to implement people detection. Say using my own dataset: 3000 positive samples and 6000 negative samples. My question is Does SVM need to do learning each time when detecting people? If so, the learning time and predicting time could be so time-consuming. Is there any way to implement real-time people detection? Thank you in advance.

Thank you for your answers. I have obtained the xml result after training(3000 positive and 6000 negative), so I can just use this result to write an other standalone program just use svm.load() and svm.predict()? That's great. Besides, I found that the predicting time for 1000 detection window size image(128x64) is also quite time-consuming(about 10 seconds), so how does it handle a normal surveillance camera capture(320x240 or higher) using 1 or 2 pixels scanning stepsize in real time?

I implemented HOG according to the original paper, 8x8 pixels per cell, 2x2 cells per block(50% overlap), so 3780 dimensions vector for one detection window(128x64). Is the time problem caused by the huge feature vector? Should I reduce the dimensions for each window?

Upvotes: 1

Views: 410

Answers (2)

berak
berak

Reputation: 39806

no, you don't have to re-train an svm each and every time.

you do the training once, then svm.save() the trained model to a xml/yml file.

later you just svm.load() that instead of the (re-)training, and do your predictions

Upvotes: 1

dubbaluga
dubbaluga

Reputation: 2343

This is a very specific question to a general topic.

Short answer: no, you don't need to learning every time you want to use a SVM. It is a two step process. The first step, learning (in your case by providing your learning algorithm with many many labeled (containing, not containing) pictures containing people or not containing people), results in a model which is used in the second step: testing (in your case detecting people).

Upvotes: 3

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