GPGPU-based real-time object detection and recognition system

Abstract

Many neuroscience inspired vision algorithms have been proposed over the past few decades. However, it is difficult to easily compare the various algorithms that have been proposed by investigators. Many are very computationally intensive and are thus hard to run at or near real time. This makes it difficult to rapidly compare different algorithms. Further, it makes it difficult to tweak existing algorithms and to design new algorithms due to the training and testing framework that must be constructed around it. With the advent of GPGPU computing significant speedups on the order of 10-50 times are achievable if the computations are intensive, local, and massively parallel. Many object recognition systems fit this description, so the GPGPU provides an attractive platform. We describe an implemented GPGPU-based system that uses saliency (Itti, Koch, 1998) to detect interesting regions of a scene, and a generic backend that can run various object recognition systems such as HMAX (Riesenhuber, Poggio 1999) or SIFT (Lowe, 2004). The less intensive front end system only achieved a speed up of 2x, but HMAX was sped up by 10x (Chikkerur, 2008). We believe that this framework will allow rapid testing and improvement of novel recognition algorithms.