MFST: Multi-Features Siamese Tracker

Abstract

We present a Multi-Features Siamese Tracker (MFST), a novel tracking algorithm exploiting several hierarchical feature maps for robust deep similarity tracking. Since Convolutional layers provide several abstraction levels in characterizing an object, fusing hierarchical features allows to ensure a richer and more efficient representation. Moreover, we handle the target appearance variation by calibrating deep features extracted from two different CNN models. Based on this advanced feature representation, our method achieves high tracking accuracy, while outperforming standard Siamese trackers on object tracking benchmarks.