InceptB: A CNN Based Classification Approach for Recognizing Traditional Bengali Games

TL;DR

This paper presents InceptB, a CNN-based model that effectively recognizes traditional Bengali games, achieving around 80% accuracy, addressing a gap in computer vision applications for regional sports recognition.

Contribution

The paper introduces a novel deep learning approach using Inception V3 architecture specifically retrained for recognizing traditional Bengali games.

Findings

Achieved approximately 80% accuracy in recognizing 5 Bengali games.

Demonstrated the effectiveness of transfer learning for regional sports classification.

Addressed the lack of computer vision research on traditional Bangladeshi sports.

Abstract

Sports activities are an integral part of our day to day life. Introducing autonomous decision making and predictive models to recognize and analyze different sports events and activities has become an emerging trend in computer vision arena. Albeit the advances and vivid applications of artificial intelligence and computer vision in recognizing different popular western games, there remains a very minimal amount of efforts in the application of computer vision in recognizing traditional Bangladeshi games. We, in this paper, have described a novel Deep Learning based approach for recognizing traditional Bengali games. We have retrained the final layer of the renowned Inception V3 architecture developed by Google for our classification approach. Our approach shows promising results with an average accuracy of 80% approximately in correctly recognizing among 5 traditional Bangladeshi sports events.