Prajna Parimita Dash
Papers
1
Total Citations
2
H-Index
1
About
Prajna Parimita Dash is a computer vision researcher whose work centers on robust object detection and tracking in video sequences—a foundational challenge for applications like smart surveillance, human-machine interaction, and robotics. Her most cited paper, "Robust detection & tracking of object by particle filter using color information" (2013), addresses the difficulty of maintaining accurate tracking over extended image sequences. By integrating particle filtering with color-based cues, Dash proposed a method that improves tracking stability under challenging conditions such as occlusion and cluttered backgrounds. While her citation count is modest, her contribution lies in tackling a persistent problem in visual tracking, offering a practical solution that balances computational efficiency with robustness. Dash’s work underscores the importance of sensor fusion and probabilistic models in real-time vision systems. For students and researchers entering the field, her research provides a clear entry point into understanding how classical tracking techniques can be adapted for modern applications. Her focus on reliability and real-world deployment highlights the ongoing need for algorithms that perform consistently in dynamic environments.
Research Focus
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Top Papers
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