Sports Recognition using Convolutional Neural Network with Optimization Techniques from Images and Live Streams


  • Shakil Ahmed Reja
  • Mohammed Mahmudur Rahman



Sport classification, Multimedia content analysis, Deep learning, Pre-trained models, Convolutional Neural Networks, VGG16, Resnet50, Model-Optimizer


This paper deals with the issue of automated image and video recognition of sports. It is a category of appreciation of human behavior, which is a very difficult task in the present day to classify images and video clips into a categorized gallery. This research paper proposes a sports detection system using a deeper CNN model that combines a fully connected layer with fine-tuning. It is applied to classify five individual sports groups through images and videos. In this work, we use a video classification method based on the image. Extended Resnet50 and VGG16 two pre-trained Deep CNNs are applied to build this sports detection system. RMSProp, ADAM & SGD optimizers are used to train the extended CNN models for five Epochs on the proposed 5sports dataset by handpicking thousands of sports images from the internet to very smoothly classify the five different types of sports. Training accuracy of approximately 83% is observed for ResNet50 with an SGD optimizer for 5 sports classes and 95% is observed for 3 sports classes.


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How to Cite

Reja, S. A., & Rahman, M. M. (2021). Sports Recognition using Convolutional Neural Network with Optimization Techniques from Images and Live Streams. International Journal of Computing, 20(2), 276-285.