We address the challenging problem of detecting and classifying speed-limit signs in a real-time video stream using an embedded,low-end GPU. We implement three pipelines to address this problem. The first is a detection-only feature-based method that finds objects with radial symmetry (suitable for circular EU-speed-limit signs). In this implementation, we leverage the graphics part of the GPU pipeline to perform the radial-symmetry voting step. The second is a template-based method that searches for image templates in the frequency domain using FFT correlations, suitable for both EU and US speed-limit signs. This method performs recognition (both detection and classification); it incorporates contrast-enhancement, composite filters, frequency-domain detection and classification, and temporal integration to aggregate results over many frames in its implementation. The third is classic GPU-based SIFT approach which provides a basis for evaluation of recognition results of the template-based approach. We show 88% detection accuracy using feature-based pipeline on an embedded system (Intel Atom CPU + NVIDIA GeForce 9200M GS GPU) running at 33 fps. In addition, we show 90% recognition accuracy using template-based pipeline on Intel Core2 Duo P8600 2.4GHz CPU and a NVIDIA GeForce 9600M GT GPU (a low-end GPU which can be used in embedded automotive system) running at 18 fps, superior in both accuracy and frame rate to the SIFT-based approach.