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EPCNet: Implementing an 'Artificial Fovea' for More Efficient Monitoring Using the Sensor Fusion of an Event-Based
Orla Sealy Phelan1,2, Dara Molloy3, Roshan George1,2
1Department of Electrical and Electronic Engineering, University of Galway, University Road, H91 TK33 Galway, Ireland.
This study fuses event-based and RGB cameras for efficient object detection. Combining these sensors improves accuracy and reduces latency in real-time applications like autonomous driving.
Area of Science:
- Computer Vision
- Robotics
- Sensor Fusion
Background:
- High-resolution RGB cameras offer accuracy but increase latency and power consumption.
- Down-sampling images for Convolutional Neural Networks (CNNs) causes information loss.
- Event-based cameras provide high temporal resolution and low power, but lack spatial detail.
Purpose of the Study:
- To mitigate the trade-off between temporal resolution and accuracy in object detection.
- To minimize latency and power consumption in real-time monitoring systems.
- To propose a novel multi-modal stereo vision system fusing event and RGB cameras.
Main Methods:
- Calibrated event and RGB cameras to create a multi-modal stereo vision system.
- Projected pixel coordinates between event and RGB camera image planes.
- Used event camera for region proposals, feeding cropped RGB frames to CNNs.
Main Results:
- Average precision (AP) for bicycles improved from 21.08 to 57.38.
- Overall AP across all classes increased from 37.93 to 46.89.
- Proposed approach achieved a 78% improvement in system latency over baseline.
Conclusions:
- Fusion of event-based and RGB cameras enhances object detection accuracy and efficiency.
- The proposed system effectively reduces latency and power consumption for real-time applications.
- Event cameras can serve as region proposal networks, optimizing CNN performance.

