  Object tracking is one of the most challenging task and has secured
significant attention of computer vision researchers in the past two decades.
Recent deep learning based trackers have shown good performance on various
tracking challenges. A tracking method should track objects in sequential
frames accurately in challenges such as deformation, low resolution, occlusion,
scale and light variations. Most trackers achieve good performance on specific
challenges instead of all tracking problems, hence there is a lack of general
purpose tracking algorithms that can perform well in all conditions. Moreover,
performance of tracking techniques has not been evaluated in noisy
environments. Visual object tracking has real world applications and there is
good chance that noise may get added during image acquisition in surveillance
cameras. We aim to study the robustness of two state of the art trackers in the
presence of noise including Efficient Convolutional Operators (ECO) and
Correlation Filter Network (CFNet). Our study demonstrates that the performance
of these trackers degrades as the noise level increases, which demonstrate the
need to design more robust tracking algorithms.
