Sensor Generalization for Adaptive Sensing in Event-based Object Detection via Joint Distribution Training
| Authors | Aheli Saha et al. |
| Year | 2026 |
| Field | Computer Vision |
| arXiv | 2602.23357 |
| Download | |
| Categories | cs.CV |
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.
Engineering Breakdown
Plain English
This paper tackles a practical problem in event camera research: models trained on event data don't generalize well across different camera hardware because the intrinsic camera parameters (like resolution, sensor size, pixel sensitivity) significantly affect the event signal characteristics. The authors provide a systematic analysis of how these parameters impact object detection performance, then propose joint distribution training—a method that trains models on data from multiple sensor configurations simultaneously to make them robust across different event cameras. Their approach enables downstream models to work reliably on unfamiliar sensor hardware without retraining, addressing a major bottleneck in deploying event-based vision systems to new devices.
Core Technical Contribution
The core novelty is twofold: first, a detailed empirical characterization of how intrinsic event camera parameters (temporal resolution, spatial resolution, contrast sensitivity thresholds, etc.) affect object detection model performance—this analysis fills a gap in the literature because event cameras are relatively new and parameter sensitivity hasn't been systematically studied. Second, the authors introduce joint distribution training, where a single model is optimized on event data generated across multiple sensor configurations, creating a parameter-agnostic representation that generalizes to unseen camera hardware. This is distinct from prior work that either assumes a fixed sensor or requires expensive domain adaptation per new camera; joint distribution training achieves sensor-agnostic robustness through multi-sensor training rather than post-hoc adaptation.
How It Works
The pipeline starts with event data collected from multiple event cameras with different intrinsic parameters—this synthetic or real multi-sensor dataset is the critical input. For each sensor configuration, events are generated with their characteristic spikes based on temporal contrast sensitivity and spatial resolution. The model (likely a standard object detector backbone adapted for event streams) ingests these event representations and is trained with a loss function that encourages consistent feature representations across all sensor configurations—essentially forcing the model to ignore sensor-specific noise and artifacts. The key mechanism is that during training, the model sees the same scene from multiple 'views' (different sensor parameters), creating an implicit constraint that forces learned features to be invariant to sensor characteristics. At inference time, the model can then handle novel event cameras without fine-tuning because it has already learned to abstract away sensor-specific variations.
Production Impact
For teams deploying event-based object detection systems in the real world, this eliminates the painful retraining loop every time hardware changes—a major pain point in robotics, autonomous vehicles, and surveillance applications. Rather than collecting and labeling data for each new camera model, engineers can now use a single pre-trained model across different event camera hardware, dramatically reducing deployment friction and data costs. The trade-off is that training requires synthetic or multi-sensor real data upfront, which increases data engineering complexity and initial training cost, but this is a one-time investment. Latency and compute requirements during inference should remain unchanged since the architecture itself isn't modified; the benefit is purely in generalization. For production systems integrating event cameras alongside RGB cameras, this approach makes event-based detection a more viable first-class component rather than a special case requiring custom per-hardware tuning.
Limitations and When Not to Use This
The paper assumes that the variability captured during training (the range of sensor parameters) will cover or bracket the parameters of sensors encountered at deployment time—if you encounter a camera with drastically different specs, generalization could degrade. The approach doesn't explicitly address domain gaps between synthetic event data and real sensor noise, if that gap exists; the method assumes the event generation model itself is accurate. Joint distribution training requires access to or the ability to simulate multiple sensor configurations during training, which may not be feasible if you're working with proprietary hardware. The paper also doesn't deeply address temporal dynamics or motion artifacts that vary with sensor frame rates, focusing primarily on spatial/contrast parameters. Follow-up work would need to characterize how many distinct sensor configurations are needed for good generalization coverage and whether synthetic event simulation is sufficient or if real multi-sensor data is essential.
Research Context
This work is positioned in the growing field of event-camera vision, which has seen increasing academic and industry interest due to neuromorphic principles and advantages like high dynamic range and low latency. It builds on prior object detection research adapted for event inputs (e.g., converting event streams to 2D representations or 3D voxel grids) and adds a critical engineering perspective: robustness across hardware variants. The paper likely benchmarks on standard datasets like N-ImageNet or DVS-based datasets but contributes primarily methodological insights about parameter sensitivity rather than state-of-the-art detection accuracy. This work opens a research direction toward hardware-agnostic sensor fusion models and may influence how the event-vision community standardizes training procedures—moving from sensor-specific models to parameter-invariant training as a best practice.
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