Yacine Moresly

Polytechnic School of Algiers, Cranfield University

Papers

Total Citations

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About

Yacine Moresly is a researcher specializing in computer vision and object tracking, with a focus on developing robust algorithms for real-time video analysis. His work centers on enhancing the accuracy and efficiency of face detection and multi-object tracking systems, addressing key challenges in dynamic environments. Moresly's most cited paper, "Face detection and tracking system with block-matching, meanshift and camshift algorithms and Kalman filter" (2017, 6 citations), introduces a novel hybrid method that integrates block-matching (BMA) for preprocessing with Meanshift, Camshift, and Kalman filter algorithms to improve tracking stability and precision. This approach demonstrates his ability to combine classical techniques for practical, real-world applications. His earlier work, "Modeling from an Object and Multi-object Tracking System" (2016, 3 citations), explores the complexities of tracking multiple moving objects in video sequences, emphasizing the need for fast, reliable solutions. While his citation counts reflect an emerging career, Moresly's contributions are notable for their methodological synthesis, offering a foundation for future advancements in surveillance, human-computer interaction, and autonomous systems. His research continues to influence students and engineers seeking efficient tracking frameworks.