Lenni Yulianti

Papers

Total Citations

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About

Lenni Yulianti is a researcher advancing the intersection of computer vision and machine learning, with a primary focus on real-time object tracking and detection. Her work addresses critical challenges in dynamic environments, particularly occlusion handling and robust performance in specialized settings. Yulianti’s most cited paper, “Deep features fusion for KCF-based moving object tracking” (2023, 14 citations), introduces a novel fusion of deep features with the Kernelized Correlation Filter (KCF) framework, enhancing tracking accuracy and real-time capability—a key contribution for applications like surveillance. Her impactful study “Centroid-Tracking-Aided Robust Object Detection for Hospital Objects” (2020, 8 citations) demonstrates applied innovation during the COVID-19 pandemic, developing a robot-based system that integrates centroid tracking to improve object detection for healthcare settings, prioritizing patient care and worker safety. With a total of 22 citations across her top works, Yulianti’s research is notable for its practical relevance, bridging algorithmic development with real-world deployment. Her achievements highlight a commitment to creating efficient, occlusion-resilient solutions that advance autonomous systems in critical sectors, making her work a valuable reference for students and researchers exploring robust, application-driven computer vision.