A model adaptive updating kernel correlation filter tracker with deep CNN features

Abstract

The tracker based on correlation filter shows excellent performance in tracking accuracy and running speed. However, the models of correlated filter trackers are always updated with fixed weights, which can degrade the tracking performance when the target in a variety of challenging scenarios. In this paper, we present a model adaptive updating method based on a fuzzy system, which can set different updating weights on each frame to effectively deal with the challenging scenarios in the tracking process. Attractively, this method can be perfectly used in all trackers based on correlation filtering. In addition, we combine deep CNN features that can describe target semantics with HOG features that have spatial descriptions. Using their complementarity to target descriptions, we establish HOG-based filter model and CNN-based filter model. To two response maps of the models, we propose a different fusion strategy based on quality measurement of tracking results, which can balance the accuracy and robustness of the tracker. Experiments on OTB-2013, OTB-2015, benchmark videos, and VOT2018 dataset show that our tracker (called MACF) is effective and exhibits competitive results compared with the recent state-of-the-art (SOTA) trackers.