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

• URL: http://arxiv.org/abs/2602.23357v1
• Date: Thu, 26 Feb 2026 18:57:52 GMT
• Title: Sensor Generalization for Adaptive Sensing in Event-based Object Detection via Joint Distribution Training
• Authors: Aheli Saha, René Schuster, Didier Stricker,
• Abstract summary: Bio-inspired event cameras have recently attracted significant research due to their asynchronous and low-latency capabilities.<n>There is a gap in the variability of available data and a lack of extensive analysis of the parameters characterizing their signals.<n>This paper addresses these issues by providing readers with an in-depth understanding of how intrinsic parameters affect the performance of a model trained on event data, specifically for object detection.
• Score: 18.51701989107632
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Bio-inspired event cameras have recently attracted significant research due to their asynchronous and low-latency capabilities. These features provide a high dynamic range and significantly reduce motion blur. However, because of the novelty in the nature of their output signals, there is a gap in the variability of available data and a lack of extensive analysis of the parameters characterizing their signals. This paper addresses these issues by providing readers with an in-depth understanding of how intrinsic parameters affect the performance of a model trained on event data, specifically for object detection. We also use our findings to expand the capabilities of the downstream model towards sensor-agnostic robustness.

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