Real-time Object Recognition on a GPU

Abstract: Shape-Based matching (SBM) is a known method for 2D object recognition that is rather robust against illumination variations, noise, clutter and partial occlusion. The objects to be recognized can be translated, rotated and scaled. The translation of an object is determined by evaluating a similarity measure for all possible positions (similar to cross correlation). The similarity measure is based on dot products between normalized gradient directions in edges. Rotation and scale is determined by evaluating all possible combinations, spanning a huge search space. A resolution pyramid is used to form a heuristic for the search that then gains real-time performance. For SBM, a model consisting of normalized edge gradient directions, are constructed for all possible combinations of rotation and scale. We have avoided this by using (bilinear) interpolation in the search gradient map, which greatly reduces the amount of storage required. SBM is highly parallelizable by nature and with our suggested improvements it becomes much suited for running on a GPU. This have been implemented and tested, and the results clearly outperform those of our reference CPU implementation (with magnitudes of hundreds). It is also very scalable and easily benefits from future devices without effort. An extensive evaluation material and tools for evaluating object recognition algorithms have been developed and the implementation is evaluated and compared to two commercial 2D object recognition solutions. The results show that the method is very powerful when dealing with the distortions listed above and competes well with its opponents.