Problem
Research questions and friction points this paper is trying to address.
spiking neural networks
energy-efficient UAV tracking
spike activity optimization
SBMVTrack: Spike-Budgeted Multi-View Learning for Energy-Efficient UAV Tracking
🤖 AI Summary
本文提出SBMVTrack，通过能量加权脉冲预算和掩码多视图目标建模方法，解决了SNN在UAV跟踪中能量效率低的问题。
📝 Abstract
With sparse and event-driven computation, spiking neural networks show great potential for achieving accurate and energy-efficient UAV visual tracking. However, existing SNN-based trackers typically use spike firing rates only for energy evaluation and lack explicit optimization of actual spike activity. To address this, we propose SBMVTrack, a fully spiking framework for energy-efficient UAV tracking. SBMVTrack introduces Energy-Weighted Spike Budgeting (EWSB). EWSB weights actual spike activity according to the computational cost of each spiking layer. It constrains the energy-weighted firing rate and saturation activity, thereby reducing redundant spike computations. To improve tracking performance under the spike budget constraint, we propose Masked Multi-View Target Modeling (MVTM). This method treats the initial template, online template, and search region from the same sequence as correlated temporal views. It enhances the robustness of target representations through cross-view feature completion and identity-consistency learning. Extensive experiments on multiple benchmarks demonstrate that SBMVTrack effectively reduces the average spike firing rate and theoretical energy consumption. Meanwhile, it maintains competitive tracking performance, achieving a better accuracy-energy trade-off. The source code will be released upon acceptance.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Energy-Weighted Spike Budgeting
Masked Multi-View Target Modeling
Spike-Budgeted Multi-View Learning
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