Event-to-Video Conversion for Overhead Object Detection
This addresses the challenge of integrating energy-efficient event cameras into complex image processing tasks for applications like surveillance or autonomous systems, though it is incremental as it builds on existing conversion techniques.
The paper tackles the problem of using event cameras for overhead object detection, finding a performance gap compared to RGB frames, and shows that converting event streams to grayscale video with event-to-video models significantly improves performance, outperforming event-specific methods.
Collecting overhead imagery using an event camera is desirable due to the energy efficiency of the image sensor compared to standard cameras. However, event cameras complicate downstream image processing, especially for complex tasks such as object detection. In this paper, we investigate the viability of event streams for overhead object detection. We demonstrate that across a number of standard modeling approaches, there is a significant gap in performance between dense event representations and corresponding RGB frames. We establish that this gap is, in part, due to a lack of overlap between the event representations and the pre-training data used to initialize the weights of the object detectors. Then, we apply event-to-video conversion models that convert event streams into gray-scale video to close this gap. We demonstrate that this approach results in a large performance increase, outperforming even event-specific object detection techniques on our overhead target task. These results suggest that better alignment between event representations and existing large pre-trained models may result in greater short-term performance gains compared to end-to-end event-specific architectural improvements.