Real-time Architecture for Robust Motion Estimation under Varying Illumination Conditions

Motion estimation from image sequences is a complex problem which requires high computing resources and is highly affected by changes in the illumination conditions in most of the existing approaches. in this contribution we present a high performance system that deals with this limitation. robustness to varying illumination conditions is achieved by a novel technique that combines a gradient-based optical flow method with a non-parametric image transformation based on the rank transform. the paper describes this method and quantitatively evaluates its robustness to different illumination changing patterns. this technique has been successfully implemented in a real-time system using reconfigurable hardware. our contribution presents the computing architecture, including the resources consumption and the obtained performance. the final system is a real-time device capable to computing motion sequences in real-time even in conditions with significant illumination changes. the robustness of the proposed system facilitates its use in multiple potential application fields.