Automatic player identification is an essential and complex task in sports video analysis. Different strategies have been devised over the years, but identification based on jersey numbers is one of the most common approaches given its versatility and relative simplicity. However, automatic detection of jersey numbers is still challenging due to changing camera angles, low video resolution, small object size in wide-range shots and transient changes in the player's posture and movement. In this paper we present a novel approach for jersey number identification in a small, highly imbalanced dataset from the Seattle Seahawks practice videos. We use a multi-step strategy that enforces attention to a particular region of interest (player's torso), to identify jersey numbers. We generate in-house synthetic datasets of different complexities to supplement the data imbalance and scarcity in the samples. Our multi-step pipeline first identifies and crops players in a frame using a pretrained person detection model. We then utilize a pretrained human pose estimation model to localize jersey numbers (using torso key-points) in the detected players, obviating the need for annotating bounding boxes for number detection. This results in images that are on average 20x25px in size. We trained two light-weight Convolutional Neural Networks (CNNs) with different learning objectives: multi-class for two-digit number identification and multi-label for digit-wise detection to compare performance. Both models went through a pre-training round with the synthetic datasets and were finetuned with the real-world dataset to achieve a final best accuracy of 89%. Our results indicate that simple models can achieve an acceptable performance on the jersey number detection task and that synthetic data can improve the performance dramatically (accuracy increase of 9% overall, 18% on low frequency numbers) making our approach achieve state of the art results.

Open-book question answering is a subset of question answering (QA) tasks where the system aims to find answers in a given set of documents (open-book) and common knowledge about a topic. This article proposes a solution for answering natural language questions from a corpus of Amazon Web Services (AWS) technical documents with no domain-specific labeled data (zero-shot). These questions have a yes–no–none answer and a text answer which can be short (a few words) or long (a few sentences). We present a two-step, retriever–extractor architecture in which a retriever finds the right documents and an extractor finds the answers in the retrieved documents. To test our solution, we are introducing a new dataset for open-book QA based on real customer questions on AWS technical documentation. In this paper, we conducted experiments on several information retrieval systems and extractive language models, attempting to find the yes–no–none answers and text answers in the same pass. Our custom-built extractor model is created from a pretrained language model and fine-tuned on the the Stanford Question Answering Dataset—SQuAD and Natural Questions datasets. We were able to achieve 42% F1 and 39% exact match score (EM) end-to-end with no domain-specific training.

Player identification is an essential and complex task in sports video analysis. Different strategies have been devised over the years and identification based on jersey numbers is one of the most common approaches given its versatility and relative simplicity. However, automatic detection of jersey numbers is challenging due to changing camera angles, low video resolution, small object size in wide-range shots, and transient changes in the player's posture and movement. In this paper, we present a novel approach for jersey number identification in a small, highly imbalanced dataset from the Seattle Seahawks practice videos. We generate novel synthetic datasets of different complexities to mitigate the data imbalance and scarcity in the samples. To show the effectiveness of our synthetic data generation, we use a multi-step strategy that enforces attention to a particular region of interest (player's torso), to identify jersey numbers. The solution first identifies and crops players in a frame using a person detection model, then utilizes a human pose estimation model to localize jersey numbers in the detected players, obviating the need for annotating bounding boxes for number detection. We experimented with two sets of Convolutional Neural Networks (CNNs) with different learning objectives: multi-class for two-digit number identification and multi-label for digit-wise detection to compare performance. Our experiments indicate that our novel synthetic data generation method improves the accuracy of various CNN models by 9% overall, and 18% on low frequency numbers.

Open book question answering is a subset of question answering tasks where the system aims to find answers in a given set of documents (open-book) and common knowledge about a topic. This article proposes a solution for answering natural language questions from a corpus of Amazon Web Services (AWS) technical documents with no domain-specific labeled data (zero-shot). These questions can have yes-no-none answers, short answers, long answers, or any combination of the above. This solution comprises a two-step architecture in which a retriever finds the right document and an extractor finds the answers in the retrieved document. We are introducing a new test dataset for open-book QA based on real customer questions on AWS technical documentation. After experimenting with several information retrieval systems and extractor models based on extractive language models, the solution attempts to find the yes-no-none answers and text answers in the same pass. The model is trained on the The Stanford Question Answering Dataset -SQuAD (Rajpurkar et al., 2016) and Natural Questions (Kwiatkowski et al., 2019) datasets. We were able to achieve 49% F1 and 39% exact match score (EM) end-to-end with no domain-specific training.

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