  Visual object tracking task is constantly gaining importance in several
fields of application as traffic monitoring, robotics, and surveillance, to
name a few. Dealing with changes in the appearance of the tracked object is
paramount to achieve high tracking accuracy, and is usually achieved by
continually learning features. Recently, deep learning-based trackers based on
LSTMs (Long Short-Term Memory) recurrent neural networks have emerged as a
powerful alternative, bypassing the need to retrain the feature extraction in
an online fashion. Inspired by the success of residual and dense networks in
image recognition, we propose here to enhance the capabilities of hybrid
trackers using residual and/or dense LSTMs. By introducing skip connections, it
is possible to increase the depth of the architecture while ensuring a fast
convergence. Experimental results on the Re3 tracker show that DenseLSTMs
outperform Residual and regular LSTM, and offer a higher resilience to
nuisances such as occlusions and out-of-view objects. Our case study supports
the adoption of residual-based RNNs for enhancing the robustness of other
trackers.
