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Recent advances in Siamese network-based visual tracking methods have enabledhigh performance on numerous tracking benchmarks. However, extensive scalevariations of the target object and distractor objects with similar categorieshave consistently posed challenges in visual tracking. To address thesepersisting issues, we propose novel TridentAlign and context embedding modulesfor Siamese network-based visual tracking methods. The TridentAlign modulefacilitates adaptability to extensive scale variations and large deformationsof the target, where it pools the feature representation of the target objectinto multiple spatial dimensions to form a feature pyramid, which is thenutilized in the region proposal stage. Meanwhile, context embedding module aimsto discriminate the target from distractor objects by accounting for the globalcontext information among objects. The context embedding module extracts andembeds the global context information of a given frame into a local featurerepresentation such that the information can be utilized in the finalclassification stage. Experimental results obtained on multiple benchmarkdatasets show that the performance of the proposed tracker is comparable tothat of state-of-the-art trackers, while the proposed tracker runs at real-timespeed.

Visual Tracking by TridentAlign and Context Embedding