Feature Harvesting for Tracking-by-Detection
We propose a fast approach to 3-D object detection and pose estimation that owes its robustness to a training phase during which the target object slowly moves with respect to the camera. No additional information is provided to the system, save a very rough initialization in the first frame of the training sequence. It can be used both to detect the target object in each frame independently and to track it from frame to frame. Our approach relies on a Randomized Tree-based approach to feature-point wide-baseline matching. Unlike previous classification-based approaches to 3-D pose estimation, we do not require an a priori 3-D model. Instead, our algorithm learns both geometry and appearance. In the process, it collects, harvests, a list of object features that can be reliably recognized even when large motions and aspect changes cause complex variations of feature appearances. This is made possible by the great flexibility of Randomized Trees, which lets us add and remove feature points to our list as needed with a minimum amount of extra computation.