RISE

Abstract

This paper proposes RISE, an automated Reconfigurable framework for real-time background subtraction applied to Intelligent video SurveillancE. RISE is devised with a new streaming-based methodology that adaptively learns/updates a corresponding dictionary matrix from background pixels as new video frames are captured over time. This dictionary is used to highlight the foreground information in each video frame. A key characteristic of RISE is that it adaptively adjusts its dictionary for diverse lighting conditions and varying camera distances by continuously updating the corresponding dictionary. We evaluate RISE on natural-scene vehicle images of different backgrounds and ambient illuminations. To facilitate automation, we provide an accompanying API that can be used to deploy RISE on FPGA-based system-on-chip platforms. We prototype RISE for end-to-end deployment of three widely-adopted image processing tasks used in intelligent transportation systems: License Plate Recognition (LPR), image denoising/reconstruction, and principal component analysis. Our evaluations demonstrate up to 87-fold higher throughput per energy unit compared to the prior-art software solution executed on ARM Cortex-A15 embedded platform.