CricketShotRecognition.mp4
- Cricket is a globally celebrated sport with profound economic impacts, involving billions in revenue and extensive fan engagement.
- AI-driven data analytics is rapidly transforming cricket, offering new avenues for player development and strategic planning.
- This project focuses on classifying cricket shots from videos into distinct categories and exploring the similarities between these shots.
- By leveraging these insights, players can enhance their skills, and coaches can identify promising new talent more effectively.
- Dataset sourced from CrickShot10 authors [1].
- Removed score texts from videos for better shot analysis.
- Applied horizontal flips to represent different batting styles.
- Divided the dataset into training, validation, and testing sets with a 70-20-10 split for effective model evaluation.
Model | Training Accuracy | Validation Accuracy |
---|---|---|
EfficientNetB0 | 100% | 85.80% |
EfficientNetV2B0 | 100% | 77.01% |
EfficientNetB4 | 100% | 72.86% |
- Built three model variants, each with a distinct feature extractor head to evaluate performance variations.
- Trained all models for 20 epochs using batch sizes of 16, processing 30 frames per video to capture temporal dynamics.
- Utilized the Adam optimizer, configured with a learning rate of 0.001, to efficiently converge to optimal weights.
- Employed sparse categorical cross-entropy as the loss function, for handling class labels as integers.
- Each individual in the population represents a set of model hyperparameters, such as learning rate and epochs.
- Individuals are assessed based on the validation accuracy of the model trained with their hyperparameters.
- Randomly selects small groups of individuals, with the best-performing individual from each group chosen to continue to the next generation.
- Combines and modifies selected individuals' hyperparameters to explore new solutions and improve model performance.
- The stagnation limit is set to 10, meaning the genetic algorithm halts if there's no improvement in the best fitness score after 10 consecutive generations.
- The learning rate ranges between 0.0001 and 0.02, and the epochs range from 1 to 20, ensuring a comprehensive exploration of the hyperparameter space.
Model | Testing Accuracy | Precision | Recall | F1 Score |
---|---|---|---|---|
EfficientNetB0 | 94% | 94% | 94% | 94% |
EfficientNetV2B0 | 81% | 82% | 81% | 81% |
EfficientNetB4 | 74% | 75% | 74% | 74% |
- All three models were evaluated on the test set.
- Accuracy, Precision, Recall, and F1-score were the metrics used for evaluation.
- The model with EfficientNet B0 backbone outperformed the other two models.
- Extracted features from the convolutional block of the EfficientNet backbone, mapping them into a concise vector representation.
- Calculated cosine distance between feature vectors to assess similarities across different video inputs.
- Utilized this distance metric to determine the degree of similarity between two cricket shot videos.
- Confirmed model accuracy with a 100% similarity score for identical input videos, validating the effectiveness of the feature extraction and comparison approach.
This project is a collaborative effort between Ritik Bompilwar and Pratheesh. Equal contributions were made in developing and optimizing the model to enhance accuracy and performance.
- A. Sen, K. Deb, P. K. Dhar, and T. Koshiba, "CricShotClassify: An Approach to Classifying Batting Shots from Cricket Videos Using a Convolutional Neural Network and Gated Recurrent Unit," Sensors, vol. 21, no. 8, Art. no. 2846, 2021. [Online]. Available: https://doi.org/10.3390/s21082846.
- M. Tan and Q. V. Le, "EfficientNet: Rethinking model scaling for convolutional neural networks," in Proc. 36th Int. Conf. Mach. Learn., Long Beach, CA, USA, 2019, vol. 97, pp. 6105–6114. [Online]. Available: http://proceedings.mlr.press/v97/tan19a.html
- K. Cho et al., "Learning phrase representations using RNN encoder-decoder for statistical machine translation," arXiv preprint arXiv:1406.1078, 2014. [Online]. Available: https://arxiv.org/abs/1406.1078
- M. Abadi et al., "TensorFlow: Large-scale machine learning on heterogeneous systems," 2016. [Online]. Software available: https://www.tensorflow.org/
- "Streamlit: The fastest way to build custom ML tools," Streamlit. Accessed: Apr. 17, 2024. [Online]. Available: https://www.streamlit.io/