FRATCF: Feature-Residue Real-Time UAV Tracking Based on Automatic Spatio-Temporal Regularization Correlation Filter

Abstract

Traditional discriminative correlation filter (DCF) has received widespread popularity due to its high computational efficiency. However, most of the existing DCF-based trackers improve the learning of the target object by introducing some simple regularization methods in the detection stage, which may easily lose the tracking target in scenes with background clutter, fast-moving cameras and similar targets. We propose a feature residual filter with automatic spatio-temporal regularization, namely FRATCF, which can be strengthened the filter learning by introducing the feature residual between two adjacent frames in the training phase. Extensive experiments are conducted on two challenging unmanned aerial vehicle (UAV) benchmarks, i.e., UAV123@10fps and DTB70. Results prove that our tracker runs at ∼43 FPS on an extremely cheap configuration, which is about twice the speed of AutoTrack. The performance is also better than other state-of-the-art (SOTA) trackers.