Proceedings Article10.1109/CVPRW.2018.00228
Convolutional Neural Networks Based Ball Detection in Tennis Games
Vito Renò,Nicola Mosca,Roberto Marani,Massimiliano Nitti,Tiziana D'Orazio,Ettore Stella +5 more
- 18 Jun 2018
- pp 1758-1764
TL;DR: An innovative deep learning approach to the identification of the ball in tennis context is presented, exploiting the potential of a convolutional neural network classifier to decide whether a ball is being observed in a single frame, overcoming the typical issues that can occur dealing with classical approaches on long video sequences.
read more
Abstract: In recent years sport video research has gained a steady interest among the scientific community. The large amount of video data available from broadcast transmissions and from dedicated camera setups, and the need of extracting meaningful information from data, pose significant research challenges. Hence, computer vision and machine learning are essential for enabling automated or semi-automated processing of big data in sports. Although sports are diverse enough to present unique challenges on their own, most of them share the need to identify active entities such as ball or players. In this paper, an innovative deep learning approach to the identification of the ball in tennis context is presented. The work exploits the potential of a convolutional neural network classifier to decide whether a ball is being observed in a single frame, overcoming the typical issues that can occur dealing with classical approaches on long video sequences (e.g. illumination changes and flickering issues). Experiments on real data confirm the validity of the proposed approach that achieves 98.77% accuracy and suggest its implementation and integration at a larger scale in more complex vision systems.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Rep-Tracker: a lightweight tiny ball tracking method using structure re-parameterization
15 May 2023
TL;DR: In this paper , a lightweight tiny ball tracking method is developed based on deep learning call Rep-Tracker, which incorporates the structure re-parameterization strategy on the basis of the pruned VGG-style model to construct a tennis tracking network.
Machine Learning Techniques for Human Activity Recognition Using Wearable Sensors
Moushumi Das,Vansh Pundir,Vandana Mohindru Sood,Kamal Deep Garg,Sushil Kumar Narang +4 more
- 01 Jan 2023
TL;DR: Human activity recognition (HAR) using wearable sensors and machine learning techniques enables improved health, quality of life, and various applications.
1
The analysis of tennis recognition model for human health based on computer vision and particle swarm optimization algorithm
TL;DR: An intelligent tennis picking robot is studied to recognize and position tennis balls and the algorithm proposed provides some ideas to solve the problem of tennis picking for tennis players.
1
Football Player and Ball Tracking System Using Deep Learning
E. S. Garai
- 01 Jan 2023
TL;DR: In this paper , a neural network is proposed to recognize players and football by giving the video of the match of any arbitrary size and length as input or in simple words our model will be able to work on videos of any size, length and quality.
1
References
ImageNet classification with deep convolutional neural networks
TL;DR: A large, deep convolutional neural network was trained to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes and employed a recently developed regularization method called "dropout" that proved to be very effective.
•Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
Alex Krizhevsky,Ilya Sutskever,Geoffrey E. Hinton +2 more
- 03 Dec 2012
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
67K
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky,Jia Deng,Hao Su,Jonathan Krause,Sanjeev Satheesh,Sean Ma,Zhiheng Huang,Andrej Karpathy,Aditya Khosla,Michael S. Bernstein,Alexander C. Berg,Li Fei-Fei +11 more
TL;DR: The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) as mentioned in this paper is a benchmark in object category classification and detection on hundreds of object categories and millions of images, which has been run annually from 2010 to present, attracting participation from more than fifty institutions.
•Journal Article
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky,Jia Deng,Hao Su,Jonathan Krause,Sanjeev Satheesh,Sean Ma,Zhiheng Huang,Andrej Karpathy,Michael S. Bernstein,Li Fei-Fei,Alexander C. Berg,Aditya Khosla +11 more
TL;DR: The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) has been running annually for five years (since 2010) and has become the standard benchmark for large-scale object recognition.
23.9K