SURFTrac: Efficient tracking and continuous object recognition using local feature descriptors

We present an efficient algorithm for continuous image recognition and feature descriptor tracking in video which operates by reducing the search space of possible interest points inside of the scale space image pyramid. Instead of performing tracking in 2D images, we search and match candidate features in local neighborhoods inside the 3D image pyramid without computing their feature descriptors. The candidates are further validated by fitting to a motion model. The resulting tracked interest points are more repeatable and resilient to noise, and descriptor computation becomes much more efficient because only those areas of the image pyramid that contain features are searched. We demonstrate our method on real-time object recognition and label augmentation running on a mobile device.