Abstract Summary/Description
Here’s a concise summary/description of the abstract: This work focuses on improving object detection and tracking efficiency using neuromorphic computing and Visible Light Communication (VLC) with the Metavision SDK. The study leverages the SDK's Python-based inference pipeline, which uses a pre-trained TorchScript model to detect and track vehicles and pedestrians. The system processes event-based data, outputs bounding boxes, and provides confidence levels for each detected object. Dual visualization panes display detection and tracking results, showcasing the system's potential for low-power, high-speed performance. The setup involves using event-based cameras or pre-recorded RAW/DAT files, along with extensive pipeline functionalities such as geometric preprocessing, noise filtering, and data association. Parameters like inference thresholds, RoI filtering, and Non-Max Suppression (NMS-IoU) can be adjusted to optimize detection and tracking performance. Performance is influenced by factors such as lighting conditions, camera placement, and lens focus. While the Python implementation is efficient, a faster C++ alternative is also available. This integration of VLC and neuromorphic processing highlights the feasibility of real-time, energy-efficient object detection, with potential applications in smart cities, IoT systems, and autonomous technologies. The study achieves results comparable to traditional methods while reducing energy consumption and improving efficiency.
