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SBMVTrack: Spike-Budgeted Multi-View Learning for Energy-Efficient UAV Tracking

arXiv · AI, language, vision and robotics · article · Sep 21, 2026 · UTC

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 firin

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