  Rotation is among the long prevailing, yet still unresolved, hard challenges
encountered in visual object tracking. The existing deep learning-based
tracking algorithms use regular CNNs that are inherently translation
equivariant, but not designed to tackle rotations. In this paper, we first
demonstrate that in the presence of rotation instances in videos, the
performance of existing trackers is severely affected. To circumvent the
adverse effect of rotations, we present rotation-equivariant Siamese networks
(RE-SiamNets), built through the use of group-equivariant convolutional layers
comprising steerable filters. SiamNets allow estimating the change in
orientation of the object in an unsupervised manner, thereby facilitating its
use in relative 2D pose estimation as well. We further show that this change in
orientation can be used to impose an additional motion constraint in Siamese
tracking through imposing restriction on the change in orientation between two
consecutive frames. For benchmarking, we present Rotation Tracking Benchmark
(RTB), a dataset comprising a set of videos with rotation instances. Through
experiments on two popular Siamese architectures, we show that RE-SiamNets
handle the problem of rotation very well and out-perform their regular
counterparts. Further, RE-SiamNets can accurately estimate the relative change
in pose of the target in an unsupervised fashion, namely the in-plane rotation
the target has sustained with respect to the reference frame.
