CricXpert: a hybrid spatial fusion model for enhanced player recognition

This paper introduces a hybrid spatial fusion model for near real-time player recognition in T20i cricket. The system combines ResNet50 for deep feature extraction fused with Support Vector Machine (SVM), K-Nearest Neighbors (KNN) base classifiers, and a Logistic Regression meta-classifier in a stacking ensemble. A novel, domain-specific dataset was developed to capture challenges such as occlusions, variable lighting, and distant camera views followed by an expert evaluation and validation of the dataset. The proposed model achieved 98.14% accuracy, 98% precision, and 98% recall, significantly outperforming standalone deep learning models. These results highlight the advantages of combining deep and traditional machine learning methods. The approach is both robust and resource-efficient, offering a practical solution for sports analytics and laying the foundation for future work on temporal data and transfer learning.