如何改进具有线性SVM性能的HOG检测器以进行汽车检测？

Question

So, I want to detect cars from a driver recorder recorded video. I've read a lot and do research quite a lot but still not quite getting it. I do think of using a HOG descriptor with linear SVM. But in what way it can still be improver to make it easier to be implement and more robust since this will be kind of a research for me? I am thinkin of combining another technique/algorithm with the HOG but still kind of lost. I am quite new in this.

Any help is greatly appreciated. I am also open to other better ideas.

Answer 1

HOG (histogram of oriented gradients) is merely a certain type of feature vector that can be computed from your data. You compute the gradient vector at each pixel in your image and then you divide up the possible angles into a discrete number of bins. Within a given image sub-region, you add the total magnitude of the gradient pointing in a given direction as the entry for the relevant angular bin containing that direction.

This leaves you with a vector that has a length equal to the number of bins you've chosen for dividing up the range of angles and acts as an unnormalized histogram.

If you want to compute other image features for the same sub-region, such as the sum of the pixels, some measurement of sharp angles or lines, aspects of the color distribution, or so forth, you can compute as many or as few as you would like, arrange them into a long vector as well, and simply concatenate that feature vector with the HOG vector.

You may also want to repeat the computation of the HOG vector for several different scale levels to help capture some scale variability, concatenating each scale-specific HOG vector onto the overall feature vector. There are other feature concepts like SIFT and others, which are created to automatically account for scale invariance.

You may need to do some normalization or scaling, which you can read about in any standard SVM guide. The standard LIBSVM guide is a great place to start.

You will have to be careful to organize your feature vector correctly since you will likely have a very large number of components to the feature vector, and you have to ensure they are always calculated and placed into the same ordering and undergo exactly the same scaling or normalization treatments.