HoG, PCA, PSO, Hard Negative Mining, Sliding Window, NMS
Best way to do detection is:
HoG(features) -> PCA(less features) + PSO(best C&gamma) -> origin SVM -> HNM(more features) -> better SVM -> SW -> NMS(bbox regression)
Sorry for my laziness.
I think I should clarify the steps for the program.
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Extract HoG features (script 1)
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Train an initial model for pso (script 2)
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Do pca and pso for better parameters C and gamma (script 6)
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Use no-pca features and the best parameters to train the second model (script 2)
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In order to increase the accuracy, use the second model to do hnm and get the final model(script 7)
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Finally, choose an algorithm you like to do location(script 8 or 9 or 10)
PS:
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The reason I use pca is to accelerate the speed of pso. To be honestly, pso is really slow.
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For step 4, you can also use features processed by pca, but I strongly advise you to hold as possible as more features. Because more features, higher accuracy.
杯子数据集(Dataset)： https://pan.baidu.com/s/18ho4UI50x4YP6lkrjPm7Kw