Event-Based Object Detection

Overview

Event-based object detection studies how to identify and localize objects from asynchronous brightness-change signals, which can capture fast motion with fine timing and little redundant data. Methods convert events into time surfaces, voxel or continuous-time representations, or process them directly with recurrent, sparse-attention, state-space, and spiking neural networks; RGB-event fusion is also used when appearance cues help. Automotive benchmarks such as Gen1 and 1Mpx support comparison, while pedestrian, drone, and small-object settings test performance under motion, low light, and sparse target evidence. A central challenge is balancing accuracy with latency and energy use while improving robustness across sensors and scenes, with benchmark coverage and real-world evaluation still important open issues.

Latest papers

CardsList

36 papers1 paper in the last four weeks, down 75% on the four weeks before. 0.0% of all new papers.

  1. ODD-SEC: Onboard Drone Detection with a Spinning Event Camera

    Mar 6, 2026Kuan Dai, Hongxin Zhang, Sheng Zhong +1UAV DetectionAerial Object Detection

  2. Low-Latency Event-Based Object Detection with Spatially-Sparse Linear Attention

    Mar 6, 2026Haiqing Hao, Zhipeng Sui, Rong Zou +4Efficient Object DetectionLinear Attention

  3. Sensor Generalization for Adaptive Sensing in Event-based Object Detection via Joint Distribution Training

    Feb 26, 2026Aheli Saha, René Schuster, Didier StrickerAdaptive SensingEvent-Based Vision

  4. Locally Adaptive Decay Surfaces for High-Speed Face and Landmark Detection with Event Cameras

    Feb 26, 2026Paul Kielty, Timothy Hanley, Peter CorcoranEvent-Based VisionEvent-Based Object Detection