Adapting Visual Trackers to Dynamic View Transitions With Shift-View Prompt Tuning

Visual tracking is essential across numerous video analysis applications, surveillance systems, entertainment, and autonomous applications. However, most conventional state-of-the- art visual trackers are designed for constant-view scenarios with fixed camera viewpoints, and they only achieve satisfactory performance under stable visual features scenarios. In reality, visual tracking often encounters shift-view scenarios (e.g., sports broadcasting, ground-aerial surveillance), where cameras’ dynamic view transitions between ground and aerial views. These shifts lead to large variations in target scale and environmental complexity, resulting in inconsistent visual features that ultimately degrade the robustness of conventional visual trackers. Although developing a dedicated tracker for such shift-view scenarios is possible, it requires expensive temporal and computational costs. To address this challenge, we propose Shift-view Prompt Tuning, a cost-efficient method that enables conventional trackers to handle dynamic view transitions. We use sample pairs from different view datasets as prompts to guide the tracker’s adaptation. By embedding distinctive visual information from these prompts into training samples, we help the tracker learn about dynamic view transitions without requiring it to be relearned from scratch. This approach seamlessly transforms any constant-view trackers into shift-view trackers. Our extensive experiments on 14 datasets with 3 different view types show that our approach significantly enhances tracking performance. This advancement extends the application scope of current trackers and offers a robust solution for multimedia content production, sports analytics, and security monitoring in video analysis systems.