Game-Changing Moments - Unleashing the Power of Computer Vision in Sports Events.

A Brief Introduction to Computer Vision and its Applications 🤖🚀

For starters, Computer Vision is one of the most compelling applications of AI, which was introduced first in the late 1960s, with the intention of computers being able to mimic the intricacies of the human visual system, thereby enabling them to identify and process visual information. Thanks to colossal volumes of visual data (over 3 billion images shared online daily) being generated today, the computing power needed for analysis has become readily available. 

The expansion of computer vision, propelled by advancements in hardware and algorithms, has resulted in improved accuracy rates for object identification. In under ten years, present-day systems have elevated their accuracy from 50 per cent to 99 per cent, surpassing human capability in swiftly responding to visual stimuli in some cases.

The prevalence of visual data in various domains has allowed for the effective resolution of numerous challenges facing humanity. In healthcare, computer vision expedites precise diagnosis and treatment planning. Urban planning benefits from traffic optimization and infrastructure development insights. Environmental monitoring uses visual data for ecosystem tracking and conservation. Across these domains, computer vision's application to visual data efficiently addresses challenges, fostering innovation and societal progress.

How Computer Vision Helps in Sports

One might be surprised to discover that Computer Vision is not impartial to the dynamic world of sports. Ardent fans never want to miss a moment of the action, especially during crucial game events. Harnessing the power of Computer Vision (CV) proves to be a game-changer in this regard. Through advanced event detection algorithms, CV can swiftly identify and highlight key moments within a game, creating concise and noteworthy clips that encapsulate the essence of the game.

Computer Vision (CV) can significantly contribute to event detection in games like cricket, basketball, soccer etc where time and placement are of paramount importance; automating the identification of key moments during a match relies heavily on well-informed annotations and a good understanding of the rules, and other nitty-gritty details of the game. Once all of the prerequisites are in place, CV works its magic by identifying events on its own thereafter, via models like YOLO (You Only Look Once). YOLO operates by dividing the input image into a grid and predicting bounding boxes and class probabilities directly for each grid cell. 

Additionally, models such as Faster R-CNN (Region-based Convolutional Neural Network) excel in precise object detection, leveraging a region proposal network for efficient event identification. SSD (Single Shot Multibox Detector) is another contender, providing rapid and accurate object detection by predicting bounding boxes and class scores at different scales in a single pass.
In this blog, we will be focusing on how CV helps effective event detection in the game of cricket.

Pyrack Leverages YOLO-v8 for Event Detection in Cricket

YOLO, or You Only Look Once, is a groundbreaking model for spotting objects swiftly and accurately. Imagine it like this: instead of a two-step process, YOLO takes in the whole picture in a single sweep through its neural network. It breaks the image into a grid and then predicts bounding boxes and class probabilities for each grid cell. What's cool is that each prediction not only gives you the object's coordinates but also a confidence score for its class. This means YOLO can spot multiple objects at once. The secret sauce? YOLO's ability to handle different object sizes and shapes in one go makes it perfect for scenarios where speed is key; case in point - a cricket match in real-time!

Here is a step-by-step overview of how we created a first-shot Cricket event detection algorithm:

Data Annotation :

We diligently annotated every frame of a real cricket match, categorising each event with labels like bowling, batting, boundary, catch, fielder, scoreboard, umpire, wicketkeeper, and wide using a data annotation tool called Roboflow.

We are required to upload the dataset for annotation. We can provide images in formats such as JPG, PNG, and BMP, as well as videos in MOV and MP4 formats. For object detection annotations, it supports various formats including JSON, XML, CSV, and TXT. The annotated dataset can be exported in the desired format chosen by us. We are required to download a folder curated by Roboflow and it comprises artefacts such as the annotated images randomly sorted into train, test and validation sets, and a YAML (data.yaml) file consisting of the relevant training-related data, such as the labels/encodings of the classes, the path to these data-sets etc. 

train: ../train/images
val: ../valid/images
test: ../test/images

nc: 6
names: ['Bowling', 'batting', 'boundary', 'catch', 'other', 'wide', 'score board']

roboflow:
  workspace: pyrack
  project: event_detection_01
  version: 1
  license: CC BY 4.0
  url: https://universe.roboflow.com/pyrack/event_detection_01/dataset/1

Training the data :

We then leveraged the Ultralytics library's YOLO v8 model and our meticulously crafted training data, achieving an impressive 75% accuracy over 100 epochs, using a very simple Linux command in Google Colab:

!yolo task=detect \
mode=train \
model=yolov8s.pt \
data=/content/drive/MyDrive/Yolo_event_detection.v1i.yolov8/data.yaml \
epochs=100 \
imgsz=640

We opted for Colab as our development environment for training data due to the invaluable inclusion of GPU support, which significantly enhances our processing capabilities.

!yolo task=detect \
mode=predict \
model='/100_epochs_weights/weights/best.pt' \
conf=0.25 \
source='/Test_videos/Virender Sehwag_Cricket World Cup 2011.mp4'

Model evaluation :

Using the bowling event as a reference point, our model intelligently cropped the video, allowing us to isolate specific events based on their occurrence. With the groundwork laid, users can now dictate their preferences. Our code, backed by the MoviePy library, facilitates the extraction and compilation of videos, tailored to the user's desired cricketing events. Whether it's a collection of thrilling catches or a showcase of powerful boundaries, the user commands, and our code delivers, ensuring a personalised and efficient video compilation experience.

Conclusion :

Our venture into the synergy of Computer Vision and sports exemplifies the disruptive potential of AI in reshaping how we engage with sports events. By automating event detection and content compilation, we not only save time but also offer fans an enriched, immersive, and tailored viewing experience. The future of sports analysis is now, and at its forefront is the captivating fusion of Computer Vision and the dynamic world of sports.

Scope :

This project endeavours to explore the transformative capabilities of Computer Vision (CV) within the domain of sports, with a specific focus on cricket event detection. The scope encompasses the application of advanced CV algorithms, notably the YOLO (You Only Look Once) model, to identify and highlight key moments during cricket matches in real-time.

The project involves a comprehensive understanding of Computer Vision principles, tracing its evolution and recent advancements, coupled with the utilisation of robust tools such as Roboflow for data annotation and the Ultralytics library for training with YOLO v8. The aim is to achieve precise event detection with a high level of accuracy.

Key Components of the Extended Scope -

1. Multi-Sport Event Detection: Extend the capabilities of the CV model, particularly YOLO v8, to accommodate a variety of sports beyond cricket. This includes, but is not limited to, basketball, soccer, tennis, and others. Each sport presents unique challenges, requiring nuanced adaptations in the annotation and training processes for accurate event detection.

2. Enhanced Data Annotation: Implement more sophisticated data annotation techniques to improve the accuracy and granularity of event identification. Beyond the initial set of events identified in cricket, consider sport-specific events such as goals, penalties, free throws, and player-specific actions, ensuring a more detailed and comprehensive annotation process.

3. Cross-Sport Model Training: Utilise advanced training methodologies to adapt the CV model for multi-sport scenarios. This involves refining the model architecture, optimising hyperparameters, and exploring transfer learning techniques to enhance the model's ability to detect diverse events across different sports.

4. Making use of Speech-to-Text models: Integrating a Speech-to-Text (STT) model such as Whisper (by Open AI) allows us to convert the entire audio feed of a match into a time stamped transcript. This transcript serves as a parallel source of information, capturing events as they happen. When synchronised with visual data, it enriches our understanding, providing a detailed account of match dynamics. 

The overarching goal is to establish a versatile and robust CV framework that can seamlessly adapt to various sports, providing sports analysts, enthusiasts, and broadcasters with a powerful tool for real-time event detection and analysis. The project's outcomes aim to contribute valuable insights and practical solutions to the evolving field of AI in sports analytics.