Zynq-Based Reconfigurable System for Real-Time Edge Detection of Noisy Video Sequences

Abstract

We implement Zynq-based self-reconfigurable system to perform real-time edge detection of 1080p video sequences. While object edge detection is a fundamental tool in computer vision, noises in the video frames negatively affect edge detection results significantly. Moreover, due to the high computational complexity of 1080p video filtering operations, hardware implementation on reconfigurable hardware fabric is necessary. Here, the proposed embedded system utilizes dynamic reconfiguration capability of Zynq SoC so that partial reconfiguration of different filter bitstreams is performed during run-time according to the detected noise density level in the incoming video frames. Pratt's Figure of Merit (PFOM) to evaluate the accuracy of edge detection is analyzed for various noise density levels, and we demonstrate that adaptive run-time reconfiguration of the proposed filter bitstreams significantly increases the accuracy of edge detection results while efficiently providing computing power to support real-time processing of 1080p video frames. Performance results on configuration time, CPU usage, and hardware resource utilization are also compared.