Live Demonstration: Real-Time Object Detection & Classification System in IoT with Dynamic Neuromorphic Vision Sensors

In this paper, we demonstrate an energy-efficient real-time object detection and classification system featuring a hybrid event-based frame generation pipeline and a background-removal region proposal algorithm. The event-based frame is generated by aggregating active events within a programmable time interval, generating an event-based binary image (EBBI). This approach enables the utilization of low-complexity algorithms for denoising and object detection. The background-removal region proposal algorithm reduces memory requirements and removes dynamic backgrounds, leading to better detection performance. The proposed system is demonstrated on Zynq-7000 FPGA device with a DAVIS346 sensor. Experimental results show that the proposed system achieves comparable detection accuracy while requiring significantly less computation than existing event-based trackers.