Event-based Selective Attention for Multi-resolution Fast Region of Interest (ROI) Detection

2026-09-15

AI Summary

研究通过多尺度无训练的显著性注意模型，直接处理低分辨率事件输入，选择感兴趣区域，以解决神经形态视觉系统在带宽、内存和能源受限条件下的数据减少和选择性处理问题。

Abstract

Neuromorphic vision systems operate under strict constraints on bandwidth, memory, and energy, particularly at the edge, motivating early mechanisms for data reduction and selective processing. In this work, we investigate a multi-scale training-free, saliency-based, bottom-up visual attention model that operates directly on low-resolution event-based input and selects Regions of Interest (ROI) from the visual scene. The model is evaluated across multiple downscaling factors applied to the incoming event stream, with input resolutions reduced by up to 256x relative to full resolution. Performance is assessed on the Prophesee Automotive dataset, the largest publicly available event-based dataset, demonstrating robust ROI selection across different scales on a real-world use-case. The proposed approach is capable of detecting ROIs belonging to multiple object classes, including various vehicle types, pedestrians, traffic lights, and traffic signs, with accuracy up to 70.8%, while operating at millisecond temporal resolution, 16x finer than the temporal resolution provided by the dataset ground truth. These results highlight the potential of combining early event downscaling with saliency-based attention as an effective front-end for efficient edge neuromorphic vision systems.

Problem

• neuromorphic vision
• data reduction
• selective processing
• event-based input
• Region of Interest (ROI)

Innovation

• Event-based
• Saliency-based Attention
• Multi-scale
• ROI Detection
• Neuromorphic Vision