SpikeTrack: A Spike-driven Framework for Efficient Visual Tracking

Spiking Neural Networks (SNNs), characterized by their event-driven computation and low power consumption, have shown great potential for energy-efficient visual tracking on unmanned aerial vehicles (UAVs). However, existing SNN-based trackers often rely on costly event cameras, which limits their deployment on standard RGB-camera UAV platforms. To address this limitation, we propose STATrack, a fully spiking neural network framework for UAV visual tracking using only RGB inputs. To the best of our knowledge, this is the first study to explore fully spiking neural networks for RGB-based UAV visual tracking. Considering the challenge of preserving fine-grained target information in deep spiking representations under a limited number of time steps, we introduce Adaptive Mutual Information Maximization (AMIM). AMIM maximizes the statistical dependency between target-centered templates and their deep spiking representations, encouraging the spiking backbone to learn representations that better distinguish the target from surrounding backgrounds. We further develop a sample-difficulty-aware dynamic weighting strategy that adaptively adjusts the mutual-information constraint according to the relative localization difficulty of each training batch. Extensive experiments on four widely used UAV tracking benchmarks demonstrate that STATrack achieves state-of-the-art tracking performance with low theoretical energy consumption, highlighting its potential for energy-constrained UAV applications. Code is released at: https://anonymous.4open.science/r/STATrack.

Multi-object tracking (MOT) plays a fundamental role in visual perception, where accurate trajectory prediction is essential for reliable target association under complex motion patterns. Recent trackers have improved motion modeling with densely activated artificial neural networks, yet they largely overlook whether such dense responses are necessary for trajectory prediction. In this paper, we formulate activation sparsity preference (ASP) by tackling two key questions: 1. How can we identify a model architecture that appropriately and formally explains ASP, and 2. How can we translate this explanation into competitive tracking performance. Theoretical analysis shows that sparse gating is no worse than state-independent dropout under the same activation rate. Based on this insight, SpikingMOT is proposed as a spike-driven tracker that adaptively models sparse trajectory dynamics with spiking neural networks (SNNs). Specifically, SpikingMOT decomposes each trajectory state into pseudo-trajectory bases and uses the current prediction error to calibrate the posterior for next-frame prediction. With this brain-inspired loop, SpikingMOT achieves state-of-the-art performance in extensive experiments, 74.9 HOTA on SportsMOT and 56.5 HOTA on DanceTrack, while reducing the parameters and energy by 72% and 86.7%, respectively. These results bring SNNs into MOT, opening a promising direction for efficient tracking.

With sparse and event-driven computation, spiking neural networks show great potential for achieving accurate and power-efficient UAV visual tracking. However, existing SNN-based trackers typically use spike firing rates only for power consumption and lack explicit optimization of actual spike activity. Moreover, regulating spike activity alone does not explicitly encourage stable target representations under partial observations and temporal appearance changes. We propose SBMVTrack, a fully spiking tracking framework that combines spike activity regulation with complementary multi-view representation learning. Specifically, SBMVTrack introduces Energy-Weighted Spike Budgeting (EWSB), which incorporates layer-wise computational costs when regulating spike firing rates and penalizing saturated activations, thereby reducing redundant spike computation. To further improve target representations under the spike budget constraint, we introduce Masked Multi-View Target Modeling (MVTM), which treats the initial template, online template, and search region as temporal views of the same target. By aligning target embeddings between masked and corresponding unmasked views and enforcing cross-view identity consistency, MVTM encourages robustness to missing local cues and temporal appearance changes. Experiments on four UAV benchmarks demonstrate competitive tracking performance with a 24.1% reduction in estimated power consumption relative to the baseline. On VisDrone2018, SBMVTrack achieves a success rate of 70.0%, exceeding SpikeTrack by 9.7 percentage points while reducing estimated power consumption by 45.7%. The source code will be released upon acceptance.