Bappaditya Debnath

Qualifications

  • Bachelor of Technology, Electrical, NIT Allahabad, India
  • MSc(Distinction), Computer Science, Loughborough University, UK
Bappaditya Debnath
Research Contact Teaching
Computer Vision, Deep learning Bappaditya Debnath
Department of Computer Science
Edge Hill University
Ormskirk
Lancashire
L39 4QPPhone: 01695 651872
Email: debnathb@edgehill.ac.uk
Office: THF09
  • Programming in Java

Bio

Bappaditya has worked in the IT industry for 9 years as computer programmer and as UNIX Systems Administrator. He returned to university to complete MSc Advanced Computer Science at Loughborough where his dissertation was on Human Pose Estimation through Deep Learning. Continuing on the same line his current research involves the use of Deep Learning to build a vision-based approach for monitoring progression of functional recovery involving activity of daily living.

Publications

  • A. Behera, A. Kiedel and B. Debnath. Context-driven Multi-stream LSTM (M-LSTM) for Recognizing Fine-Grained Activity of Drivers. In 40th German Conference on Pattern Recognition (GCPR) 2018, pp. 298-314. ISSN 0302-9743 DOI https://doi.org/10.1007/978-3-030-12939-2_21.
  • B. Debnath, M. Yamaguchi, M. O’Brien and A. Behera. Adapting MobileNets for mobile based upper body pose estimation. In 15th IEEE International Conference on Advanced Video and Signal-based Surveillance (AVSS) 2018. https://doi.org/10.1109/AVSS.2018.8639378
  • Z. Wharton, E. Thomas and A. Behera. A Vision-based Transfer Learning Approach for Recognizing Behavioural Symptoms in People with Dementia. In 15th IEEE International Conference on Advanced Video and Signal-based Surveillance (AVSS) 2018. https://doi.org/10.1109/AVSS.2018.8639371

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