Learning feature transforms for object detection from panoramic images

We present a novel technique to detect objects from panoramic images using existing object detectors trained from perspective images. By leveraging existing object detectors, we save the cost of training a new detector which requires tedious and time consuming training data collection and labeling. The core of our technique is learning a feature transform which is represented by Gaussian Process Regression (GPR). Feature vectors computed directly from panoramic images are transformed into new feature vectors in such a way that the existing classifier has much better detection rate on the transformed feature vectors. Our feature transform has the interesting property that it not only corrects for the geometric distortions resulted from panoramic imaging process, but also corrects for the pose mismatches between the objects on the panoramic images and those on the training images. Our experiments show that we are able to successfully apply an existing car detector trained on perspective images to panoramic images which have both geometric distortions and larger pose variations.