SDTrack: A Baseline for Neuromorphic Tracking via Spiking Neural Networks

We establish comprehensive benchmarks for event-based camera and spiking-camera tracking.

For the event-based camera component, please refer to SDTrack-Event.

For the spike camera component, please refer to SDTrack-Spike.

News

2025-03-09

SDTrack preprint is now available on arXiv.

2025-03-15

SDTrack code is now available.

2025-08-14

🏆 🏆 🏆 We extended SDTrack to spiking cameras, introduced three datasets generated using spiking-camera simulators, and provided the first tracking baseline for spiking cameras. This work received the Best Dataset & Benchmark Award (sole winner) in the IJCAI 2025 SpikeCV–Wuji Challenge (Dataset & Benchmark Track), along with a $1,500 prize. We thank the SpikeCV organizing committee.

2025-09-19

At NeurIPS 2026, 4 accepted papers (NeurIPS-1, NeurIPS-2, NeurIPS-3, NeurIPS-4) built upon our model or adopted our reported baseline. Congratulations to the authors, and thank you for your interest!

2025-12-19

A neuromorphic tracking system built upon the DAVIS346 camera, the SDTrack pipeline, and Lynxi chips was showcased at the SLAI (Shenzhen Loop Area Institute) – LIMA (Language, Intelligence and Machines) Center Open Day. Link

Tracker Overview

Citation

If you use our tracker(SDTrack), Event Aggregation method(GTP), reported experimental results, or any other original content from this work, please cite our paper:

@article{shan2025sdtrack,
  title={Sdtrack: A baseline for event-based tracking via spiking neural networks},
  author={Shan, Yimeng and Ren, Zhenbang and Wu, Haodi and Wei, Wenjie and Zhu, Rui-Jie and Wang, Shuai and Zhang, Dehao and Xiao, Yichen and Zhang, Jieyuan and Shi, Kexin and others},
  journal={arXiv preprint arXiv:2503.08703},
  year={2025}
}

Handling of errors caused by device-related issues.

1. 'local-rank' error.

Change the code in 'SDTrack/lib/train/run_training.py'

parser.add_argument('--local_rank', default=-1, type=int, help='node rank for distributed training')

to

parser.add_argument('--local-rank', default=-1, type=int, help='node rank for distributed training')

2. 'torch.six' Error.

Change the code in 'SDTrack/lib/train/data/loader.py'

from torch._six import string_classes

to

string_classes = str

3. Frequent warning reports.

/xxx/SDTrack/lib/train/../../lib/train/data/loader.py:88: UserWarning: An output with one or more elements was resized since it had shape [3145728], which does not match the required output shape [1, 16, 3, 256, 256]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at /opt/conda/conda-bld/pytorch_1720538622298/work/aten/src/ATen/native/Resize.cpp:28.)

Add the code in the top of '/SDTrack/lib/train/run_training.py'

import warnings
warnings.filterwarnings("ignore", category=UserWarning)

4. Error with newer PyTorch versions: weights_only parameter in torch.load

[rank0]:        (1) In PyTorch 2.6, we changed the default value of the `weights_only` argument in `torch.load` from `False` to `True`. Re-running `torch.load` with `weights_only` set to `False` will likely succeed, but it can result in arbitrary code execution. Do it only if you got the file from a trusted source.
[rank0]:        (2) Alternatively, to load with `weights_only=True` please check the recommended steps in the following error message.
[rank0]:        WeightsUnpickler error: Unsupported global: GLOBAL argparse.Namespace was not an allowed global by default. Please use `torch.serialization.add_safe_globals([argparse.Namespace])` or the `torch.serialization.safe_globals([argparse.Namespace])` context manager to allowlist this global if you trust this class/function.

Solution: Add the parameter weights_only=False to all torch.load functions.