E-TIDE: Fast, Structure-Preserving Motion Forecasting from Event Sequences

arXiv cs.RO / 3/31/2026

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Key Points

E-TIDE is a new lightweight, end-to-end trainable model for predicting future event-tensor representations from past event-camera sequences without requiring large-scale pretraining.
The method uses the TIDE module, combining large-kernel temporal mixing and activity-aware gating to capture temporal dependencies efficiently for sparse event tensors.
Experiments on standard event-based datasets show competitive performance while using significantly smaller model size and reduced training requirements.
The work targets resource-constrained, real-time deployments (tight latency and memory budgets) and supports downstream tasks like future semantic segmentation and object tracking.
By focusing on structure-preserving motion forecasting from event sequences, the approach addresses the limitations of prior state-of-the-art methods that often use heavy backbones or extensive pretraining.

Abstract

Event-based cameras capture visual information as asynchronous streams of per-pixel brightness changes, generating sparse, temporally precise data. Compared to conventional frame-based sensors, they offer significant advantages in capturing high-speed dynamics while consuming substantially less power. Predicting future event representations from past observations is an important problem, enabling downstream tasks such as future semantic segmentation or object tracking without requiring access to future sensor measurements. While recent state-of-the-art approaches achieve strong performance, they often rely on computationally heavy backbones and, in some cases, large-scale pretraining, limiting their applicability in resource-constrained scenarios. In this work, we introduce E-TIDE, a lightweight, end-to-end trainable architecture for event-tensor prediction that is designed to operate efficiently without large-scale pretraining. Our approach employs the TIDE module (Temporal Interaction for Dynamic Events), motivated by efficient spatiotemporal interaction design for sparse event tensors, to capture temporal dependencies via large-kernel mixing and activity-aware gating while maintaining low computational complexity. Experiments on standard event-based datasets demonstrate that our method achieves competitive performance with significantly reduced model size and training requirements, making it well-suited for real-time deployment under tight latency and memory budgets.