  Deep Convolutional Neural Networks (DCNN) have been proven to be effective
for various computer vision problems. In this work, we demonstrate its
effectiveness on a continuous object orientation estimation task, which
requires prediction of 0 to 360 degrees orientation of the objects. We do so by
proposing and comparing three continuous orientation prediction approaches
designed for the DCNNs. The first two approaches work by representing an
orientation as a point on a unit circle and minimizing either L2 loss or
angular difference loss. The third method works by first converting the
continuous orientation estimation task into a set of discrete orientation
estimation tasks and then converting the discrete orientation outputs back to
the continuous orientation using a mean-shift algorithm. By evaluating on a
vehicle orientation estimation task and a pedestrian orientation estimation
task, we demonstrate that the discretization-based approach not only works
better than the other two approaches but also achieves state-of-the-art
performance. We also demonstrate that finding an appropriate feature
representation is critical to achieve a good performance when adapting a DCNN
trained for an image recognition task.
