I am currently doing a research project into the effectiveness of different keypoint detector and feature descriptor combinations for the task of vehicle detection.

At the moment I am using a SVM for simple binary classification of vehicles testing both the bag-of-features model and pyramid match kernels for mapping the set of vectors detected to a single feature space.

I only have a limited dataset of around 5 minutes of video captured from a UAV, of which I have manually extracted the vehicle images and rotated them so that the cars direction is horizontal. Currently I am evaluating the performance of the different detector and descriptor combos using the F-measure (F1 score). However with the aim to find a combo that are invariant to image transformations, scale, rotation, illumination/lighting variations, colour and slight affine transformations as to generalize well and be sufficiently unique to maximise the accuracy of classification, should I also be evaluating the F-measure in different conditions such as scale changes, rotation, perspective rotation, camera yaw, motion blur, and varying static and dynamic lighting conditions.

However with a limited dataset I am finding it difficult to to understand how I should go about this. For example would it be appropriate to rotate my test set by varying degrees and then use the evaluated f-measures to compare the feature detector/descriptor combos accuracy under different rotation?

Any recommendations of how I should approach the evaluation/comparison of the keypoint detector and feature descriptors under these situations would be appreciated.


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.