We propose a novel traffic sign detection system that simultaneously estimates the location and precise boundary of traffic signs using a convolutional neural network (CNN). Estimating the accurate boundary of traffic signs is important in navigation systems for intelligent vehicles where traffic signs can be used as 3-D landmarks for the road environment. Previous traffic sign detection systems, including current methods based on CNN, only provide bounding boxes of traffic signs as output and thus require additional processes such as contour estimation or image segmentation to obtain the precise boundary of signs. In this paper, the boundary estimation of traffic signs is formulated as a 2D pose and shape class prediction problem, and a single CNN effectively solves this. With the predicted 2-D pose and the shape class of a targeted traffic sign in the input, we estimate the actual boundary of the target sign by projecting the boundary of a corresponding template sign image into the input image plane. By formulating the boundary estimation problem as a CNN-based pose and shape prediction task, our method is end-to-end trainable, and more robust to occlusion and small targets than other boundary estimation methods that rely on contour estimation or image segmentation. With our architectural optimization of the CNN-based traffic sign detection network, the proposed method shows a detection frame rate higher than seven frames/second while providing highly accurate and robust traffic sign detection and boundary estimation results on a low-power mobile platform.