arXiv Computer Vision
Sep 7

Efficient Multi-Timescale Event Representations for Feed-Forward Object Detection

The paper introduces a confidence‑normalized continuous multi‑timescale representation for event cameras, using logarithmic B‑spline temporal encoding and a geometry‑aware local confidence mechanism. When paired with a fixed feed‑forward EventCenterNet detector, this representation outperforms the compact CSTR representation on the PEDRo and Gen1 datasets. Additionally, a recursive exponential‑polynomial approximation is proposed to allow efficient event‑by‑event updates while maintaining detection performance.

By Fredrik Lundell, Per-Erik Forssen, M{\aa}rten Wadenb\"ack, Astrid Lundmark
arXiv AI
Jul 13

Event Stream based Multi-Modal Video Anomaly Detection: A Benchmark Dataset and Algorithms

arXiv:2607. 09114v1 Announce Type: cross Abstract: Video anomaly detection (VAD) is critical for automated surveillance but remains fragile under challenging conditions such as illumination variations, fast motion, and complex backgrounds when relying solely on visible light videos.

By Peipei Zhu, Yueqing Niu, Lin Zhu, Guanchong Niu, Yang Yu, Zheng Li
arXiv Computer Vision
22h ago

An Event Preserving Velocity Invariant Representation for Event Cameras

The paper introduces SCARF, a real‑time velocity‑invariant representation for event cameras that preserves raw events while handling fast motion, stationary scenes, and independently moving objects. Unlike previous methods that convert events into image‑like forms and lose temporal detail, SCARF maintains temporal information and achieves state‑of‑the‑art performance in computational efficiency and representation quality.

By Mikihiro Ikura, Luna Gava, Jiahang Wu, Chiara Bartolozzi, Arren Glover