Gene

Video tracking

Scale (ratio)

Pure mathematics

Messenger RNA

Similarity (geometry)

Cartography

BitTorrent tracker

Biochemistry

Computer science

Eye tracking

Scaling

Rotation (mathematics)

Translation (biology)

Equivariant map

Physics

Trajectory

Pattern recognition (psychology)

Mathematics

Astronomy

Geography

Image (mathematics)

Object (grammar)

Artificial intelligence

Geometry

Chemistry

Computer vision

Siamese trackers turn tracking into similarity estimation between a template and the candidate regions in the frame. Mathematically, one of the key ingredients of success of the similarity function is translation equivariance. Non-translation-equivariant architectures induce a positional bias during training, so the location of the target will be hard to recover from the feature space. In real life scenarios, objects undergo various transformations other than translation, such as rotation or scaling. Unless the model has an internal mechanism to handle them, the similarity may degrade. In this paper, we focus on scaling and we aim to equip the Siamese network with additional built-in scale equivariance to capture the natural variations of the target a priori. We develop the theory for scale-equivariant Siamese trackers, and provide a simple recipe for how to make a wide range of existing trackers scale-equivariant. We present SE-SiamFC, a scale-equivariant variant of SiamFC built according to the recipe. We conduct experiments on OTB and VOT benchmarks and on the synthetically generated T-MNIST and S-MNIST datasets. We demonstrate that a built-in additional scale equivariance is useful for visual object tracking.

Scale Equivariance Improves Siamese Tracking