Yashesh Joshi
Papers
1
Total Citations
3
H-Index
1
About
Yashesh Joshi is a researcher specializing in computer vision, with a particular focus on object detection and tracking under challenging real-world conditions. His work addresses critical limitations in singular object tracking (SOT), especially when dealing with occlusion, varying illumination, and contrast changes—scenarios that often degrade the performance of conventional tracking algorithms. Joshi’s most cited paper, “Object Tracking in Occlusion and Contrast Conditions using Patch-wise Sparse Method” (2020, 3 citations), introduces a novel patch-wise sparse representation approach that enhances tracking robustness by decomposing the target into local patches and applying sparse coding to handle partial occlusions and lighting variations. This contribution is particularly valuable for applications in human-computer interaction, anonymous surveillance, and robotic vision, where reliable tracking is essential. Though his citation count is modest, Joshi’s work demonstrates a clear, practical impact by tackling a persistent problem in visual tracking. His research is a solid starting point for students and engineers seeking to understand sparse methods for robust object tracking in non-ideal environments.
Research Focus
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Top Papers
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