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Inspired by a blog post by Dale Markowitz, I used Google's Person Detection API (part of their Video Intelligence API) to create a machine learning model to recognise and classify three different cricket shots.

In my previous article, I discussed a paper which used a Long Short Term Memory network to predict which shot a batsman would play in cricket (The paper, The article), given a specific match scenario. I was keen to use an LSTM for my own purposes and by luck I came across Dale's blog and it encouraged me to use the Person Detection API for my own means.

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To create the training data, I recorded myself playing three different cricket shots; the backfoot defensive, the forward defensive and the cover drive. These were shots that I could consistently perform with similar motions which is an important consideration when training the model. Also, the forward defensive and cover drive have similar motions so it's a useful challenge to see if the algorithm could differentiate between the two shots consistently.

Results: 53% of the injuries occurred following ball impact to either the helmet faceguard and peak, or the faceguard alone. Ten injuries (29%) resulted from the ball penetrating the gap between the helmet peak and faceguard. 29% of the injuries involved the ball contacting the face following penetration of the gap between the helmet peak and faceguard. Fractures, lacerations and contusions were the most common injuries associated with face or faceguard impacts while concussion was more commonly associated with impacts to the side or rear of the helmet shell. Many of the injuries described resulted in prolonged or permanent absence from cricket.

Conclusions: Significant head and facial injuries occur in cricket batters despite wearing of helmets. Cricket helmet design and associated National and International Safety Standards should be improved to provide increased protection against head injury related to ball impact to the faceguard and shell of the helmet.

Cricket videos require analysis to develop an unbiased, equitable, and sensor-based commentary system. Moreover, detecting cricket on the basis of different types of cricket shots can be helpful for both coaches and sports analysts. Coaches and cricket experts have to regularly understand the weaknesses of different teams and adjust their game plans on the basis of those findings. In this context, it is vital to extract information from cricket data. Considering all the mentioned aspects, we chose cricket as the base sport and considered 10 different batting shots: cover drive, defensive shot, flick shot, hook, late cut, lofted shot, pull, square cut, straight drive, and sweep. The similarity of frames between different videos makes it challenging to accurately categorize batting shots. The leading contributions of our work are summarized below:

In [6], eight different cricket shots were classified using deep convolutional neural networks. A 2D CNN, 3D CNN, and long short-term memory (LSTM) recurrent neural network (RNN) were used to categorize different cricket shots. Transfer learning from a pretrained VGG model was also used. The highest accuracy of 90% was achieved using a dataset of 800 cricket shots. However, the best model had a low accuracy of 85% for the hook shot. In [7], a motion-estimation approach was proposed to classify cricket shots. Eight classes of angle ranges were defined to detect cricket shots. Accuracy was comparatively much lower: the highest accuracy of 63.57% was achieved for the off drive, and 53.32% accuracy was achieved for the hook. In [8], wearable technology was introduced to classify various cricket shots. The fundamental objective of that work was to make a quality-assessment system of various cricket shots. Hierarchical representation was used on the basis of specific aspects of cricket shots such as foot position and shot direction. Five levels of grouping actions were classified using decision tree (DT), support vector machine (SVM), and k-nearest-neighbor (k-NN) algorithms. An 88.30% class-weighted F1 score was attained using the best-performing classifiers per level. By ignoring the assessment using the real match videos, this work only considers videos collected from a training session. Foysal et al. [9] proposed a CNN model to classify six different cricket shots. They experimented on their custom-developed dataset. Semwal et al. [10] proposed a model to detect batting shots: straight drive, cover drive, and lofted on drive for both left- and right-handed batsmen. A pretrained CNN and multiclass SVM were utilized. Their proposed model attained 83.098% accuracy for 3 categories of right-handed shots and 65.186% accuracy for 3 categories of left-handed shots. The proposed model misclassified a few shots such as left cover drive, left straight drive, and right straight drive due to the limited dataset.

Quite a few methods were attempted to incorporate different events in cricket videos [11,12]. Harikrishna et al. [11] proposed an approach that initially segmented a cricket video into shots and then used an SVM to classify the detected shot into 4 events, namely, run, four, six, out. With the proposed approach, recorded accuracy was 87.8%. Their proposed model had inferior accuracy for the six events. Kolekar et al. [12] proposed a hierarchical framework for detecting cricket events: excitement detection, replay detection, field-view detection, field-view classification, close-up detection, close-up classification, and crowd classification. The authors mentioned misclassification near the shot boundaries. Premaratne et al. [13] proposed a structural approach for identifying various cricket events from a full-length cricket match. For detecting events, k-nearest-neighbor, sequential-minimal-optimization, decision-tree, and naive Bayes classifiers were used. However, the authors mentioned human interpretation for removing replay frames and highlights between delivery.

Watching sports videos over streaming sites and television network is one of the most entertaining ways to engage with sports activities. Sports videos like cricket has been viewed by larger audiences than viewing in person. The pandemic since 2020 has changed the world of sports viewing to a larger extent. Some of the sports events are even streamed live through YouTube. The most interesting part of any sports videos is watching the highlights or events of great interest. This is because of the lack of time to watch the entire length of the game. Automatic video summarization is the solution to this. Some of the day long sports like cricket needs the summarization to be very precise and bring the content within few minutes to the audience. There are several attempts in the literature to automatically summarize the sports videos, particularly the game of cricket. In this paper, an attempt has been made to review some of the latest developments in creating the video summary of cricket sports. A brief review of existing methods of video summarization that addresses many sports including soccer, cricket, tennis, and basketball are reviewed at the beginning. Later, the methods that are developed based on latest machine learning and high-performance algorithms are discussed in detail. Towards the end of this paper, a comparison of these methods is presented. The goal is to lead the prospective researchers in the direction where the methods have open avenues and scope to strengthen.

Rob Moody also known professionally by his stage name Robelinda or Robelinda2 (born 23 November 1977) is an Australian YouTuber, cricket enthusiast, freestyle archive collector, editor and guitarist. He is well known in the cricketing circles especially among the ardent cricket fans for his huge collection of old cricket content and coverage and videos.[2] He owns and runs a YouTube channel titled Robelinda2 which he uses to upload several cricket footages and he is also often called by the name of his YouTube channel than his real name.[3] His YouTube channel Robelinda2 is also regarded as the largest ever cricket archival channel in the world. It is also believed that he in fact has a larger collection of cricket archives in his possession than the combined videos possessed by the International Cricket Council and other respective cricket boards.[4][5]

Robelinda2 is also often deemed as the most sought-after YouTube cricket channel over the years. Some analysts, experts and critics consider him as someone who had contributed immensely to the prosperity and growth of cricket way beyond the efforts of global cricketing body, International Cricket Council. He is also considered as cricket's greatest librarian and also fondly remembered as cricket's YouTube hero.[6] As of February 2022, he has approximately 1.01 million subscribers. In around 2020 with the onset of the COVID-19 pandemic, there was a significant spike in the number views of his YouTube content videos and his videos became increasingly popular among cricket fans as COVID-19 brought international live cricket action to a standstill.[7][8]

He initially started the idea of sharing archived videos on YouTube by uploading some random Sheffield Shield highlights on YouTube on the request of his online friend.[13] He created his first YouTube Channel titled robelinda on 7 November 2006 but he later opened a new channel named as robelinda2 due to the difficulties he had to deal with when uploading long videos after a few months of time. He started uploading cricket content from 1980s related to Australian cricket in YouTube and also went onto upload cricket videos related to other nations afterwards.[14]

The video footage which he shared about the century scored by former Australian cricketer Greg Blewett against England in one of the test matches way back in 1998 was the first real breakthrough behind the origin story of his YouTube channel. The video he posted about David Saker's half volley bouncer to Jeff Vaughan in a test match on 10 November 2010 had raked the most number of YouTube views for any YouTube video on his channel.[15] 006ab0faaa