Unified Real-Time Tracking and Recognition with Rotation-Invariant Fast Features

We present a method that unifies tracking and video content recognition with applications to Mobile Augmented Reality (MAR). We introduce the Radial Gradient Transform (RGT) and an approximate RGT, yielding the Rotation-Invariant, Fast Feature (RIFF) descriptor. We demonstrate that RIFF is fast enough for real-time tracking, while robust enough for large scale retrieval tasks. At 26× the speed, our trackingscheme obtains a more accurate global affine motionmodel than the Kanade Lucas Tomasi (KLT) tracker. The same descriptors can achieve 94% retrieval accuracy from a database of 104 images.

• Type: Conference
• Year: 2010
• Where: CVPR
• Authors: Gabriel Takacs, Vijay Chandrasekhar, Sam Tsai, David Chen, Radek Grzeszczuk, Bernd Girod
• Keywords: Approximate Rgt, Computer Vision, CVPR 2010, Radial Gradient Transform, Scale Retrieval Tasks