  We propose a novel traffic sign detection system that simultaneously
estimates the location and precise boundary of traffic signs using
convolutional neural network (CNN). Estimating the precise boundary of traffic
signs is important in navigation systems for intelligent vehicles where traffic
signs can be used as 3D landmarks for road environment. Previous traffic sign
detection systems, including recent methods based on CNN, only provide bounding
boxes of traffic signs as output, and thus requires additional processes such
as contour estimation or image segmentation to obtain the precise sign
boundary. In this work, the boundary estimation of traffic signs is formulated
as a 2D pose and shape class prediction problem, and this is effectively solved
by a single CNN. With the predicted 2D pose and the shape class of a target
traffic sign in an input image, 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. The proposed
method with architectural optimization provides an accurate traffic sign
boundary estimation which is also efficient in compute, showing a detection
frame rate higher than 7 frames per second on low-power mobile platforms.
