Towards Low-Latency Event Stream-based Visual Object Tracking: A Slow-Fast Approach

• URL: http://arxiv.org/abs/2505.12903v1
• Date: Mon, 19 May 2025 09:37:23 GMT
• Title: Towards Low-Latency Event Stream-based Visual Object Tracking: A Slow-Fast Approach
• Authors: Shiao Wang, Xiao Wang, Liye Jin, Bo Jiang, Lin Zhu, Lan Chen, Yonghong Tian, Bin Luo,
• Abstract summary: We propose a novel Slow-Fast Tracking paradigm that flexibly adapts to different operational requirements, termed SFTrack.<n>The proposed framework supports two complementary modes, i.e., a high-precision slow tracker for scenarios with sufficient computational resources, and an efficient fast tracker tailored for latency-aware, resource-constrained environments.<n>Our framework first performs graph-based representation learning from high-temporal-resolution event streams, and then integrates the learned graph-structured information into two FlashAttention-based vision backbones.
• Score: 32.91982063297922
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing tracking algorithms typically rely on low-frame-rate RGB cameras coupled with computationally intensive deep neural network architectures to achieve effective tracking. However, such frame-based methods inherently face challenges in achieving low-latency performance and often fail in resource-constrained environments. Visual object tracking using bio-inspired event cameras has emerged as a promising research direction in recent years, offering distinct advantages for low-latency applications. In this paper, we propose a novel Slow-Fast Tracking paradigm that flexibly adapts to different operational requirements, termed SFTrack. The proposed framework supports two complementary modes, i.e., a high-precision slow tracker for scenarios with sufficient computational resources, and an efficient fast tracker tailored for latency-aware, resource-constrained environments. Specifically, our framework first performs graph-based representation learning from high-temporal-resolution event streams, and then integrates the learned graph-structured information into two FlashAttention-based vision backbones, yielding the slow and fast trackers, respectively. The fast tracker achieves low latency through a lightweight network design and by producing multiple bounding box outputs in a single forward pass. Finally, we seamlessly combine both trackers via supervised fine-tuning and further enhance the fast tracker's performance through a knowledge distillation strategy. Extensive experiments on public benchmarks, including FE240, COESOT, and EventVOT, demonstrate the effectiveness and efficiency of our proposed method across different real-world scenarios. The source code has been released on https://github.com/Event-AHU/SlowFast_Event_Track.

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