Algorithms regarding detection of object

Question

I am trying to detect the vehicles for that i used the SURF/SIFT and BOW with SVM approach but vehicles are of different type and i just studies that SURF/SIFT is for one specific object detection like usb, mobile phone etc. Is that also mean that it also affect (in detection) different types of car's like toyota and bmw etc ? or vehicles like truck and car ?

If we provide a large number of dataset of 10/15 different vehicles to SURF/SIFT then isn't it give the decent result with detection of different type of vehicles using real time approach?

Answer 1

SURF/SIFT are spatial local features. If the dataset is large enough the result should be good. Even for different vehicles only specific structures of that vehicle are available in a scene image.

However false positives might creep in if similar non-vehicle structure is present. (E.g distorted image of a small rectangular house with fence). So, some global feature like road detection might increase accuracy.

So, i think sirf/surf features of vehicles with single class SVM should help if the false positives are not present in the image of your application.

Answer 2

It seems that features and possibly a processing method are chosen incorrectly. Features, for example, should be representative for all classes of cars and thus cannot be particular interest points with descriptors representing just some peculiarities of gradient around the point and matching looking for exact spacial configuration of points relative to other points.

The processing with SVM means you classify cars against all other objects. I am not sure how you are going to get “all other objects” supporting vectors though. Much more sensible features are the same the humans seems to use to detect cars, try HOG paper for example that uses mixture of deformable parts. This closer to the sate of the art and got multiple awards so there is no need to invent a bicycle.