Problem
Research questions and friction points this paper is trying to address.
Event-based detection
Tiny object
Motion continuity
Sparse events
Asynchronous events
PointEvent: Rethinking Event-based Tiny Object Detection via Serialized Motion Evidence Accumulation
🤖 AI Summary
本文提出PointEvent方法，通过序列化运动证据积累解决远距离微小目标检测中事件稀疏、碎片化问题，提高检测精度和效率。
📝 Abstract
Event cameras offer high temporal resolution and motion sensitivity for tiny UAV detection, yet distant targets generate sparse and fragmented events that are easily overwhelmed by clutter and ego-motion. Existing methods mainly rely on dense event representations or local sparse spatiotemporal modeling, resulting in redundant computation or fragmented modeling of motion continuity across distant asynchronous events. To address this limitation, we introduce serialized motion evidence accumulation, which treats motion continuity as an ordered evidence propagation process. Specifically, the same event stream is organized into locality-preserving spatiotemporal paths and chronology-preserving temporal paths through the latent complementary serializations. Based on this principle, we propose PointEvent, a lightweight event-wise state-space framework that alternates serialized scans across the complementary orders, progressively consolidating fragmented motion evidence beyond fixed local neighborhoods. A high-resolution event branch preserves fine-grained target responses, while compact context modulation suppresses interference. Experiments demonstrate that PointEvent achieves SOTA with the fewest parameters and fastest measured inference among the compared methods. Code: https://github.com/wzz-z/PointEvent
Innovation
Methods, ideas, or system contributions that make the work stand out.
serialized motion evidence accumulation
spatiotemporal paths
temporal paths
state-space framework
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