Md. Akib Shahriar Khan

Work place: Faculty of Science and Technology, American International University – Bangladesh (AIUB), Dhaka, Bangladesh



Research Interests: Pattern Recognition, Neural Networks, Artificial Intelligence


Md. Akib Shahriar Khan is an undergraduate student enrolled at the Computer Science and Engineering program at the Faculty of Information Science and Technology of the American International University Bangladesh. Currently he is also a Research Assistant in the Department of Computer Science, American International University - Bangladesh. His research interests and passions are mostly based on Computer Vision and Pattern Recognition, Artificial Intelligence, Action Recognition and Motion Analysis, Neural Network and Machine Learning

Author Articles
Aggressive Action Estimation: A Comprehensive Review on Neural Network Based Human Segmentation and Action Recognition

By A. F. M. Saifuddin Saif Md. Akib Shahriar Khan Abir Mohammad Hadi Rahul Prashad Karmoker Julian Gomes

DOI:, Pub. Date: 8 Jan. 2019

Human action recognition has been a talked topic since machine vision was coined. With the advent of neural networks and deep learning methods, various architectures were suggested to address the problems within a context. Convolutional neural network has been the primary go-to architecture for image segmentation, flow estimation and action recognition in recent days. As the problem itself is an extended version of various sub-problems, such as frame segmentation, spatial and temporal feature extraction, motion modeling and action classification as a whole, some methods reviewed in this paper addressed sub-problems and some tried to address a single architecture to the action recognition problem. While being a success, convolution neural networks have drawbacks in its pooling methods. CapsNet, on the other hand, uses squashing function to determine the activation. Also it addresses spatiotemporal information with the normalized vector maps while CNN-based methods extracts feature map for spatial and temporal information and later augment them in a fusion layer for combining two separate feature maps. Critical review of papers provided in this work can contribute significantly in addressing human action recognition problem as a whole.

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