  Recent advances in Siamese network-based visual tracking methods have enabled
high performance on numerous tracking benchmarks. However, extensive scale
variations of the target object and distractor objects with similar categories
have consistently posed challenges in visual tracking. To address these
persisting issues, we propose novel TridentAlign and context embedding modules
for Siamese network-based visual tracking methods. The TridentAlign module
facilitates adaptability to extensive scale variations and large deformations
of the target, where it pools the feature representation of the target object
into multiple spatial dimensions to form a feature pyramid, which is then
utilized in the region proposal stage. Meanwhile, context embedding module aims
to discriminate the target from distractor objects by accounting for the global
context information among objects. The context embedding module extracts and
embeds the global context information of a given frame into a local feature
representation such that the information can be utilized in the final
classification stage. Experimental results obtained on multiple benchmark
datasets show that the performance of the proposed tracker is comparable to
that of state-of-the-art trackers, while the proposed tracker runs at real-time
speed.
