Learning Discriminative Features for Visual Tracking via Scenario Decoupling

Abstract

Visual tracking aims to estimate object state automatically in a video sequence, which is challenging especially in complex scenarios. Recent Transformer-based trackers enable the interaction between the target template and search region in the feature extraction phase for target-aware feature learning, which have achieved superior performance. However, visual tracking is essentially a task to discriminate the specified target from the backgrounds. These trackers commonly ignore the role of background in feature learning, which may cause backgrounds to be mistakenly enhanced in complex scenarios, affecting temporal robustness and spatial discriminability. To address the above limitations, we propose a scenario-aware tracker (SATrack) based on a specifically designed scenario-aware Vision Transformer, which integrates a scenario knowledge extractor and a scenario knowledge modulator. The proposed SATrack enjoys several merits. Firstly, we design a novel scenario-aware Vision Transformer for visual tracking, which can decouple historic scenarios into explicit target and background knowledge to guide discriminative feature learning. Secondly, a scenario knowledge extractor is designed to dynamically acquire decoupled and compact scenario knowledge from video contexts, and a scenario knowledge modulator is designed to embed scenario knowledge into attention mechanisms for scenario-aware feature learning. Extensive experimental results on nine tracking benchmarks demonstrate that SATrack achieves new state-of-the-art performance with high FPS.