Introduction

The convergence of artificial intelligence and sports analytics is revolutionizing performance enhancement. This project explores the application of AI in badminton, utilizing advanced player detection models such as YOLO (NAS, v5, v3), Keras, TensorFlow, and MediaPipe. The goal is to develop a robust machine learning model capable of identifying shots and analyzing player patterns.

By leveraging these state-of-the-art detection models, the system can accurately track player movements and classify various shot types from pre-recorded videos. This detailed analysis offers valuable insights into player behaviors and strategies, serving as a powerful resource for athletes seeking to refine their techniques and for coaches aiming to design more effective training programs.

While the current focus is on analyzing uploaded videos, the potential applications extend far beyond this. Future advancements could enable real-time implementation, providing immediate feedback to players during training sessions or matches.

This project marks a significant milestone in utilizing machine learning to decode the complexities of player performance. It presents a transformative tool that holds promise for both professional and amateur badminton players, setting a new standard for innovation in sports performance analysis.

Software

This project employs advanced AI models and frameworks to achieve precise player movement and shot analysis necessary for a badminton AI model. Using YOLO models (NAS, v5, v3) for real-time object detection, Keras for building and training custom models, TensorFlow for efficient processing, and Mediapipe for detailed pose estimation, the software tracks player positions and actions throughout the game. These tools collectively enable the analysis of pre-recorded badminton matches, providing comprehensive insights into player performance and shot classification.

YOLO models

The YOLO models (NAS and v5) follows a one-stage object detection  approach, directly predicting bounding boxes and class probabilities  from an input image without the need for a separate region proposal  network. Its architecture consists of three main components: a  backbone network, a neck network, and a detection head. The  backbone network, typically based on a convolutional neural network  (CNN), extracts features from the input image at multiple scales,  capturing various levels of detail. These features are then passed to  the neck network, which fuses the multi-scale features to generate  more contextually rich representations. This fusion enhances the  model's ability to detect objects at different scales and aspect ratios.  The detection head receives the fused features and performs the final  predictions, including bounding box coordinates (x, y, width, height)  and class probabilities for each potential object in the image. Non maximum suppression (NMS) is applied to remove redundant  detections and retain the most confident and accurate bounding  boxes.  To train YOLO model, a labeled dataset containing images and  corresponding bounding box annotations is required. In this project,  the bounding boxes were assigned specific labels to classify different  badminton shots: 0 representing smashes, 1 representing lobs, and 2  representing net shots. This labeling system allows the model to  accurately identify and differentiate between various types of shots  during analysis.

MEDIAPIPE Model

Mediapipe is an open-source framework designed for building perception pipelines, particularly well-suited for real-time pose estimation  and gesture recognition. In this project, Mediapipe was utilized to track and analyze player movements and shot types in badminton.  The framework employs a series of high-fidelity models to detect and annotate key points on the human body, which are then used to  infer player actions and postures.  To effectively train the Mediapipe model, a comprehensive dataset was created that included both image data and corresponding  positional data. The process involved the following steps:  

1. Image Annotation: Images collected from badminton matches were annotated with bounding boxes indicating the location of the  players. Each image was associated with a CSV file containing the xyz coordinates of key landmarks on the players' bodies. These  landmarks include crucial points such as joints and limbs, which are essential for accurate pose estimation.  

2. Labeling Shots: Each annotated image was labeled according to the type of shot being executed. The labels assigned were 0 for  smashes, 1 for lobs, and 2 for net shots. This labeling system allowed the Mediapipe model to learn and distinguish between different  shot types based on the detected movements and postures.  

3. Data Processing: The Mediapipe framework processed the annotated images and positional data to detect the human figures and  identify key landmarks. The model used these key points to understand the player's motion patterns and classify the shots accordingly.  The xyz coordinates provided a detailed spatial representation of the player's body, enhancing the model's ability to accurately track  and analyze movements.  

4. Model Training: The collected images and positional data, along with their respective labels, were used to train the Mediapipe  model. The training process involved feeding the model with annotated images and corresponding landmark data, allowing it to learn  the associations between different shot types and specific movement patterns.  

By comparing the accuracy of Mediapipe's shot classification with that of the YOLO v5 model, a comprehensive evaluation of model  performance was conducted. This comparison helps in understanding the strengths and limitations of each approach, contributing to  the development of a robust badminton performance analysis system. Through detailed landmark detection and shot classification,  Mediapipe provides valuable insights into player performance, complementing the capabilities of the YOLO v5 model.

Result

Both the YOLOv5 and Mediapipe models have shown promising potential in identifying different badminton shots, indicating their capability in tracking and  analyzing player movements. However, their accuracy is currently limited by the amount of available training data. With a larger and more diverse dataset,  these models can be further refined to provide more reliable and precise shot classification. This project marks a significant step forward, showcasing the  potential of AI in revolutionizing sports analytics and paving the way for future advancements.

Conclusion and Future Outlook

The attempt to use AI to analyze player shots and ultimately patterns in badminton is heading in a  promising direction. Although the current models require more data to achieve higher accuracy, the  foundation laid by this project is strong. With further data collection and refinement, these models have  the potential to be applied in real-time scenarios. Future implementations could utilize high-speed  cameras to track players live during matches, providing immediate feedback and strategic insights.  Additionally, expanding the system to track multiple players simultaneously will enhance its applicability  in doubles matches and training sessions. While the project is currently constrained to analyzing single  players for simplicity, this work represents a crucial stepping stone in the scientific study of AI-driven  sports analytics. The advancements made here pave the way for more sophisticated and  comprehensive performance analysis tools that could revolutionize the way athletes and coaches  approach the game.