Papers
3
Total Citations
18
H-Index
3
About
Afef Salhi is a researcher specializing in computer vision, with a particular focus on object detection, tracking, and motion estimation. Her work centers on enhancing the accuracy and robustness of tracking systems through the integration of classical algorithms with the Kalman filter, a tool she has extensively applied to solve prediction and correction tasks in dynamic visual environments. Her most-cited paper, "Estimation for Motion in Tracking and Detection Objects with Kalman Filter" (2020, 9 citations), demonstrates the filter’s optimal application in computer vision for tracking and motion analysis. She further advanced the field with her study on face detection and tracking (2017, 6 citations), where she combined block-matching, Meanshift, and Camshift algorithms with the Kalman filter to create a novel, multi-algorithm approach. Her earlier work on multi-object tracking (2016, 3 citations) tackled the challenging problem of robust, efficient tracking in video sequences. Salhi’s contributions are notable for their practical integration of established techniques, offering reliable solutions for real-world tracking and detection tasks.
Research Focus
Key Achievements
Top Papers
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- 3Modeling from an Object and Multi-object Tracking System3 citations · 2016