Capsule Network with Its Limitation, Modification, and Applications—A Survey

Haq, Mahmood Ul; Sethi, Muhammad Athar Javed; Rehman, Atiq Ur

doi:10.3390/make5030047

Cited by 8 publications

(1 citation statement)

References 99 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Henceforth, there arises a necessity to construct innovative neural network architectures based on interpretable modules to supplant conventional networks. Capsule networks, as introduced in [10][11][12], have been employed in image classification tasks. Capsules constitute a collection of neurons designed to emulate distinct entities or components of entities.…”

Section: Introductionmentioning

confidence: 99%

Interpretable Multi-Channel Capsule Network for Human Motion Recognition

Li,

Fei,

Chen

et al. 2023

Electronics

View full text Add to dashboard Cite

Recently, capsule networks have emerged as a novel neural network architecture for human motion recognition owing to their enhanced interpretability compared to traditional deep learning networks. However, the characteristic features of human motion are often distributed across distinct spatial dimensions and existing capsule networks struggle to independently extract and combine features across multiple spatial dimensions. In this paper, we propose a new multi-channel capsule network architecture that extracts feature capsules in different spatial dimensions, generates a multi-channel capsule chain with independent routing within each channel, and culminates in the aggregation of information from capsules in different channels to activate categories. The proposed structure endows the network with the capability to independently cluster interpretable features within different channels; aggregates features across channels during classification, thereby enhancing classification accuracy and robustness; and also presents the potential for mining interpretable primitives within individual channels. Experimental comparisons with several existing capsule network structures demonstrate the superior performance of the proposed architecture. Furthermore, in contrast to previous studies that vaguely discussed the interpretability of capsule networks, we include additional visual experiments that illustrate the interpretability of the proposed network structure in practical scenarios.

show abstract

Section: Introductionmentioning

confidence: 99%

Interpretable Multi-Channel Capsule Network for Human Motion Recognition

Li,

Fei,

Chen

et al. 2023

Electronics

View full text Add to dashboard Cite

show abstract

Automatic Player Face Detection and Recognition for Players in Cricket Games

Haq,

Sethi,

Ahmad

et al. 2024

IEEE Access

Self Cite

View full text Add to dashboard Cite

In this Paper, we create an augmented reality cricket broadcasting application that uses player recognition and automatic detection during play to display player personal data. The system uses AdaBoost to detect player and player face and use PAL based face recognition model to recognize the faces of players on the field. The system is trained on a large dataset of cricket game footage and achieves high accuracy in detecting and recognizing players' faces even with several conditions such as occlusion, non-uniform illumination and pose variation. The system has the potential to enhance the viewing experience of cricket games by providing real-time player identification and statistics. The system can also be used in other sports to provide similar benefits. The paper discusses the system's methodology, results, and implications for the future of sports broadcasting. Overall, the system provides a promising solution for automatic player face detection and recognition in sports broadcasting.

show abstract