Advancing Object Detection for Autonomous Vehicles via General Purpose Event-RGB Fusion

Real-time vision applications such as object detection for autonomous navigation have recently witnessed the emergence of neuromorphic or event cameras, thanks to their high dynamic range, high temporal resolution and low latency. In this work, our objective is to leverage the distinctive properties of asynchronous events and static texture information of conventional frames. Towards this goal, asynchronous events are first transformed into a 2D spatial grid representation, which is carefully selected to harness the high temporal resolution of event streams and align with conventional image-based vision. Via a joint detection framework, detections from both RGB and event modalities are fused by probabilistically combining scores and bounding boxes. The superiority of the proposed method is demonstrated over concurrent Event-RGB fusion methods on DSEC-MOD and PKU-DDD17 datasets.