This paper proposes a fast approach for traffic sign detection from video. First, we modify the image-based detector HHVCas to improve its accuracy and speed, then apply it to video-based detection with further acceleration by tracking. For the image-based detector, by optimizing the parameters in the cascade using an unsupervised approach, we achieve performance comparable to the state-of-the-art while keeping the speed advantage. Parallelizing some steps in the HHVCas detector leads to 1:5x speedup and 20fps detection. In video, the detector achieves 2.8x speedup and performs 35 fps by tracking every other frame. It also obtains significant precision increase by 5~8% at high recall when exploiting temporal coherence of results in multiple frames.