# SDTrack: A Baseline for Event-based Tracking via Spiking Neural Networks

arXiv: https://arxiv.org/abs/2503.08703 · HTML: https://mlsanity.com/paper/2503.08703/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 62 references that are in the map.
- First posted on arXiv 9 Mar 2025.

## Builds on (most cited first, 30 of 39)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [CornerNet: Detecting Objects as Paired Keypoints](https://mlsanity.com/paper/1808.01244/) (2018)
- [MetaFormer Is Actually What You Need for Vision](https://mlsanity.com/paper/2111.11418/) (2021)
- [Transformer Tracking](https://mlsanity.com/paper/2103.15436/) (2021)
- [Learning Spatio-Temporal Transformer for Visual Tracking](https://mlsanity.com/paper/2103.17154/) (2021)
- [Learning Discriminative Model Prediction for Tracking](https://mlsanity.com/paper/1904.07220/) (2019)
- [Joint Feature Learning and Relation Modeling for Tracking: A One-Stream Framework](https://mlsanity.com/paper/2203.11991/) (2022)
- [ATOM: Accurate Tracking by Overlap Maximization](https://mlsanity.com/paper/1811.07628/) (2018)
- [MixFormer: End-to-End Tracking with Iterative Mixed Attention](https://mlsanity.com/paper/2203.11082/) (2022)
- [Spikformer: When Spiking Neural Network Meets Transformer](https://mlsanity.com/paper/2209.15425/) (2022)
- [Direct Training for Spiking Neural Networks: Faster, Larger, Better](https://mlsanity.com/paper/1809.05793/) (2018)
- [Probabilistic Regression for Visual Tracking](https://mlsanity.com/paper/2003.12565/) (2020)
- [EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras](https://mlsanity.com/paper/1802.06898/) (2018)
- [Event-based Vision meets Deep Learning on Steering Prediction for Self-driving Cars](https://mlsanity.com/paper/1804.01310/) (2018)
- [Spike-driven Transformer](https://mlsanity.com/paper/2307.01694/) (2023)
- [MetaFormer Baselines for Vision](https://mlsanity.com/paper/2210.13452/) (2022)
- [VisEvent: Reliable Object Tracking via Collaboration of Frame and Event Flows](https://mlsanity.com/paper/2108.05015/) (2021)
- [ODTrack: Online Dense Temporal Token Learning for Visual Tracking](https://mlsanity.com/paper/2401.01686/) (2024)
- [Spike-driven Transformer V2: Meta Spiking Neural Network Architecture Inspiring the Design of Next-generation Neuromorphic Chips](https://mlsanity.com/paper/2404.03663/) (2024)
- [Generalized Relation Modeling for Transformer Tracking](https://mlsanity.com/paper/2303.16580/) (2023)
- [Object Tracking by Jointly Exploiting Frame and Event Domain](https://mlsanity.com/paper/2109.09052/) (2021)
- [Event Stream-based Visual Object Tracking: A High-Resolution Benchmark Dataset and A Novel Baseline](https://mlsanity.com/paper/2309.14611/) (2023)
- [HIPTrack: Visual Tracking with Historical Prompts](https://mlsanity.com/paper/2311.02072/) (2023)
- [Integer-Valued Training and Spike-Driven Inference Spiking Neural Network for High-performance and Energy-efficient Object Detection](https://mlsanity.com/paper/2407.20708/) (2024)
- [Scaling Spike-driven Transformer with Efficient Spike Firing Approximation Training](https://mlsanity.com/paper/2411.16061/) (2024)
- [Exploring Lightweight Hierarchical Vision Transformers for Efficient Visual Tracking](https://mlsanity.com/paper/2308.06904/) (2023)
- [Frame-Event Alignment and Fusion Network for High Frame Rate Tracking](https://mlsanity.com/paper/2305.15688/) (2023)

## Built on by (most cited first, 12 of 12)

- [Fully Spiking Neural Networks for Unified Frame-Event Object Tracking](https://mlsanity.com/paper/2505.20834/) (2025)
- [SpikeTrack: A Spike-driven Framework for Efficient Visual Tracking](https://mlsanity.com/paper/2602.23963/) (2026)
- [TP-Spikformer: Token Pruned Spiking Transformer](https://mlsanity.com/paper/2603.00527/) (2026)
- [Towards Low-Latency Event Stream-based Visual Object Tracking: A Slow-Fast Approach](https://mlsanity.com/paper/2505.12903/) (2025)
- [Training-Free ANN-to-SNN Conversion for High-Performance Spiking Transformer](https://mlsanity.com/paper/2508.07710/) (2025)
- [Neural Dynamics Self-Attention for Spiking Transformers](https://mlsanity.com/paper/2603.19290/) (2026)
- [eSkiTB: A Synthetic Event-based Dataset for Tracking Skiers](https://mlsanity.com/paper/2601.06647/) (2026)
- [STATrack: A Target-Aware Fully Spiking Neural Network for Efficient UAV Tracking](https://mlsanity.com/paper/2603.27493/) (2026)
- [Mask IPL: Noise-Free Intrinsic Position Learning via Computation Graph Clipping for Event-Based Spike-Driven Tracking](https://mlsanity.com/paper/2609.18716/) (2026)
- [Spike-driven Vision-Language-Action Model](https://mlsanity.com/paper/2609.39514/) (2026)
- [Contrastive Attention Mitigates Spectral Bias in Spiking Transformers](https://mlsanity.com/paper/2610.01403/) (2026)
- [SpikeOcc: Towards Resilient Multimodal 3D Occupancy Perception via Spiking Experts](https://mlsanity.com/paper/2610.15745/) (2026)

## Abstract (arXiv)

Event cameras provide superior temporal resolution, dynamic range, energy efficiency, and pixel bandwidth. Spiking Neural Networks (SNNs) naturally complement event data through discrete spike signals, making them ideal for event-based tracking. However, current approaches combining Artificial Neural Networks (ANNs) and SNNs suffer from suboptimal architectures that compromise energy efficiency and limit tracking performance. To address these limitations, we propose the first Transformer-based Spike-Driven Tracking (SDTrack) pipeline. It incorporates a novel event frame aggregation method called Global Trajectory Prompt (GTP) and a Transformer-based tracker. The GTP method effectively captures global trajectory information and aggregates it with event streams into event frames to enhance spatiotemporal representation. The Transformer-based tracker comprises a fully spike-driven SNN backbone and a simple tracking head. The SDTrack pipeline operates end-to-end without data augmentation or post-processing. Extensive experiments demonstrate that our SDTrack-Tiny pipeline achieves competitive accuracy with only 19.61M parameters and 8.16mJ energy consumption, while our Base version achieves state-of-the-art accuracy across three datasets. Our work establishes a solid foundation for future neuromorphic vision research.