Anirban Sarkar
Papers
1
Total Citations
11
H-Index
1
About
Anirban Sarkar is a researcher whose work lies at the intersection of computer vision, machine learning, and activity recognition, with a particular focus on first-person (egocentric) video analysis. His most-cited paper, "PSO based combined kernel learning framework for recognition of first-person activity in a video" (2018, 11 citations), introduces a novel framework that leverages Particle Swarm Optimization to combine multiple kernel functions for more accurate recognition of activities from a first-person perspective. This contribution addresses a key challenge in egocentric vision—how to effectively integrate diverse visual features to capture the nuances of human actions as seen from the wearer’s viewpoint. Sarkar’s work is notable for its innovative use of optimization techniques to enhance kernel learning, offering a scalable and robust solution for activity recognition in real-world scenarios. With a growing citation impact, his research continues to influence the development of intelligent systems for wearable cameras, robotics, and human-computer interaction. Sarkar’s dedication to advancing machine learning methodologies for video understanding marks him as a promising voice in the field, bridging theoretical advances with practical applications in autonomous perception.
Research Focus
Key Achievements
Top Papers
- 1