scholarly journals Semantic Extraction of Basketball Game Video Combining Domain Knowledge and In-Depth Features

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yufeng Du ◽  
Quan Zhao ◽  
Xiaochun Lu
Keyword(s):  
Domain Knowledge ◽  
Team Sports ◽  
Time Range ◽  
Target Movement ◽  
Stream Network ◽  
Video Footage ◽  
Basketball Game ◽  
Spatiotemporal Feature ◽  
High Level ◽  
Semantic Event

The team sports game video features complex background, fast target movement, and mutual occlusion between targets, which poses great challenges to multiperson collaborative video analysis. This paper proposes a video semantic extraction method that integrates domain knowledge and in-depth features, which can be applied to the analysis of a multiperson collaborative basketball game video, where the semantic event is modeled as an adversarial relationship between two teams of players. We first designed a scheme that combines a dual-stream network and learnable spatiotemporal feature aggregation, which can be used for end-to-end training of video semantic extraction to bridge the gap between low-level features and high-level semantic events. Then, an algorithm based on the knowledge from different video sources is proposed to extract the action semantics. The algorithm gathers local convolutional features in the entire space-time range, which can be used to track the ball/shooter/hoop to realize automatic semantic extraction of basketball game videos. Experiments show that the scheme proposed in this paper can effectively identify the four categories of short, medium, long, free throw, and scoring events and the semantics of athletes’ actions based on the video footage of the basketball game.

Heart Rate Recovery Changes during Competition Period in High-Level Basketball Players

2018 ◽  
Vol 1 (80) ◽  
Author(s):  
Audrius Gocentas ◽  
Anatoli Landõr ◽  
Aleksandras Kriščiūnas
Keyword(s):  
Heart Rate ◽  
Heart Rate Recovery ◽  
Team Sports ◽  
Basketball Players ◽  
Coeffi Cients ◽  
Competition Period ◽  
Monitoring Training ◽  
Intense Training ◽  
High Level

Research background and hypothesis. Replete schedule of competitions and intense training are features of contemporary team sports. Athletes, especially the most involved ones, may not have enough time to recover. As a consequence, aggregated fatigue can manifest in some undesirable form and affect athlete’s performance and health.Research aim. The aim of this study was to evaluate the changes in heart rate recovery (HRR) and investigate possible relations with sport-specifi c measures of effi cacy in professional basketball players during competition season.Research methods. Eight male high-level basketball players (mean ± SD, body mass, 97.3 ± 11.33 kg; height 2.02 ± 0.067 m, and age 23 ± 3.12 years) were investigated. The same basketball specifi c exercise was replicated several times from September till April during the practice sessions in order to assess the personal trends of HRR. Heart rate monitoring was performed using POLAR TEAM SYSTEM. Investigated athletes were ranked retrospectively according to the total amount of minutes played and the coeffi cients of effi cacy. Research results. There were signifi cant differences in the trends of HRR between the investigated players. The most effective players showed decreasing trends of HRR in all cases of ranking.Discussion and conclusions. Research fi ndings have shown that the quality of heart rate recovery differs between basketball players of the same team and could be associated with sport-specifi c effi cacy and competition playing time.Keywords: adaptation, autonomic control, monitoring training.

Understanding of social behaviour in human collective motions with non-trivial rule of control

Impact ◽  
2019 ◽  
Vol 2019 (10) ◽  
pp. 84-86
Author(s):  
Keisuke Fujii
Keyword(s):  
Machine Learning ◽  
Team Sports ◽  
Machine Learning Techniques ◽  
Theoretical Modelling ◽  
Video Footage ◽  
Learning Techniques ◽  
Collective Motions ◽  
Tracking Movements ◽  
Or Team ◽  
Computational Ability

The coordination and movement of people in large crowds, during sports games or when socialising, seems readily explicable. Sometimes this occurs according to specific rules or instructions such as in a sport or game, at other times the motivations for movement may be more focused around an individual's needs or fears. Over the last decade, the computational ability to identify and track a given individual in video footage has increased. The conventional methods of how data is gathered and interpreted in biology rely on fitting statistical results to particular models or hypotheses. However, data from tracking movements in social groups or team sports are so complex as they cannot easily analyse the vast amounts of information and highly varied patterns. The author is an expert in human behaviour and machine learning who is based at the Graduate School of Informatics at Nagoya University. His challenge is to bridge the gap between rule-based theoretical modelling and data-driven modelling. He is employing machine learning techniques to attempt to solve this problem, as a visiting scientist in RIKEN Center for Advanced Intelligence Project.

scholarly journals Natural Language Description of Videos for Smart Surveillance

2021 ◽  
Vol 11 (9) ◽  
pp. 3730
Author(s):  
Aniqa Dilawari ◽  
Muhammad Usman Ghani Khan ◽  
Yasser D. Al-Otaibi ◽  
Zahoor-ur Rehman ◽  
Atta-ur Rahman ◽  
...  
Keyword(s):  
Natural Language ◽  
Feature Recognition ◽  
Scene Recognition ◽  
Video Data ◽  
Surveillance Video ◽  
Video Footage ◽  
Parallel Pipeline ◽  
September 11 Attacks ◽  
Description Framework ◽  
High Level

After the September 11 attacks, security and surveillance measures have changed across the globe. Now, surveillance cameras are installed almost everywhere to monitor video footage. Though quite handy, these cameras produce videos in a massive size and volume. The major challenge faced by security agencies is the effort of analyzing the surveillance video data collected and generated daily. Problems related to these videos are twofold: (1) understanding the contents of video streams, and (2) conversion of the video contents to condensed formats, such as textual interpretations and summaries, to save storage space. In this paper, we have proposed a video description framework on a surveillance dataset. This framework is based on the multitask learning of high-level features (HLFs) using a convolutional neural network (CNN) and natural language generation (NLG) through bidirectional recurrent networks. For each specific task, a parallel pipeline is derived from the base visual geometry group (VGG)-16 model. Tasks include scene recognition, action recognition, object recognition and human face specific feature recognition. Experimental results on the TRECViD, UET Video Surveillance (UETVS) and AGRIINTRUSION datasets depict that the model outperforms state-of-the-art methods by a METEOR (Metric for Evaluation of Translation with Explicit ORdering) score of 33.9%, 34.3%, and 31.2%, respectively. Our results show that our framework has distinct advantages over traditional rule-based models for the recognition and generation of natural language descriptions.

Optical Music Analysis for Printed Music Score and Handwritten Music Manuscript

2011 ◽  
pp. 108-127 ◽  
Author(s):  
Kia Ng
Keyword(s):  
Domain Knowledge ◽  
Imaging System ◽  
Data Representation ◽  
Divide And Conquer ◽  
Music Score ◽  
Segmentation Approach ◽  
Printed Music ◽  
High Level ◽  
Machine Readable ◽  
Readable Format

This chapter describes an optical document imaging system to transform paper-based music scores and manuscripts into machine-readable format and a restoration system to touch-up small imperfections (for example broken stave lines and stems), to restore deteriorated master copy for reprinting. The chapter presents a brief background of this field, discusses the main obstacles, and presents the processes involved for printed music scores processing; using a divide-and-conquer approach to sub-segment compound musical symbols (e.g., chords) and inter-connected groups (e.g., beamed quavers) into lower-level graphical primitives (e.g., lines and ellipses) before recognition and reconstruction. This is followed by discussions on the developments of a handwritten manuscripts prototype with a segmentation approach to separate handwritten musical primitives. Issues and approaches for recognition, reconstruction and revalidation using basic music syntax and high-level domain knowledge, and data representation are also presented.

scholarly journals Helping Novices Avoid the Hazards of Data: Leveraging Ontologies to Improve Model Generalization Automatically with Online Data Sources

AI Magazine ◽  
2016 ◽  
Vol 37 (2) ◽  
pp. 19-32 ◽  
Author(s):  
Sasin Janpuangtong ◽  
Dylan A. Shell
Keyword(s):  
Domain Knowledge ◽  
Large Scale ◽  
Model Building ◽  
Data Extraction ◽  
Data Sources ◽  
Online Data ◽  
Model Generalization ◽  
Improve Model ◽  
Building Process ◽  
High Level

The infrastructure and tools necessary for large-scale data analytics, formerly the exclusive purview of experts, are increasingly available. Whereas a knowledgeable data-miner or domain expert can rightly be expected to exercise caution when required (for example, around fallacious conclusions supposedly supported by the data), the nonexpert may benefit from some judicious assistance. This article describes an end-to-end learning framework that allows a novice to create models from data easily by helping structure the model building process and capturing extended aspects of domain knowledge. By treating the whole modeling process interactively and exploiting high-level knowledge in the form of an ontology, the framework is able to aid the user in a number of ways, including in helping to avoid pitfalls such as data dredging. Prudence must be exercised to avoid these hazards as certain conclusions may only be supported if, for example, there is extra knowledge which gives reason to trust a narrower set of hypotheses. This article adopts the solution of using higher-level knowledge to allow this sort of domain knowledge to be used automatically, selecting relevant input attributes, and thence constraining the hypothesis space. We describe how the framework automatically exploits structured knowledge in an ontology to identify relevant concepts, and how a data extraction component can make use of online data sources to find measurements of those concepts so that their relevance can be evaluated. To validate our approach, models of four different problem domains were built using our implementation of the framework. Prediction error on unseen examples of these models show that our framework, making use of the ontology, helps to improve model generalization.

scholarly journals Creating Motivational Language Learning Experiences during the “Critical Period”

2019 ◽  
Vol 11 (3) ◽  
pp. 26
Author(s):  
Junqing Jia
Keyword(s):  
Foreign Language ◽  
Language Learning ◽  
Language Proficiency ◽  
Critical Period ◽  
Domain Knowledge ◽  
Advanced Level ◽  
Domain Specific ◽  
Language Learning Motivation ◽  
Domain Expertise ◽  
High Level

Few studies have touched upon language learning motivation of advanced-level learners of Chinese, even fewer have proposed a pedagogical framework to understand and create motivational pathways. This paper aims to fill the gap by addressing a critical period of foreign language training where students are transforming from learning the foreign language to learning domain knowledge in the foreign language. Having drawn upon Confucian concepts and contextualized curricular examples, this paper proposes a framework suggesting that learners at this stage experience a less discussed psychological complexity due to their high level of language proficiency and lack of multilingual domain capacities. They are also gradually transforming into autonomous language users who expand their social milieu through demonstrating domain expertise. As such, the pedagogical implications place an emphasis on helping advanced-level Chinese learners to establish domain-specific vision and linguistic capability so that they can perform in multicultural contexts. In particular, motivational pathways during this stage should be constructed to encourage learners to constantly reflect on their recent past self and establish visions of the future one.

scholarly journals RTPO: A Domain Knowledge Base for Robot Task Planning

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1105 ◽  
Author(s):  
Sun ◽  
Zhang ◽  
Chen
Keyword(s):  
Knowledge Base ◽  
Domain Knowledge ◽  
Task Planning ◽  
Specific Domain ◽  
Goal State ◽  
Ontology Language ◽  
Planning Algorithm ◽  
Domain Knowledge Base ◽  
Robot Task ◽  
High Level

Knowledge can enhance the intelligence of robots’ high-level decision-making. However, there is no specific domain knowledge base for robot task planning in this field. Aiming to represent the knowledge in robot task planning, the Robot Task Planning Ontology (RTPO) is first designed and implemented in this work, so that robots can understand and know how to carry out task planning to reach the goal state. In this paper, the RTPO is divided into three parts: task ontology, environment ontology, and robot ontology, followed by a detailed description of these three types of knowledge, respectively. The OWL (Web Ontology Language) is adopted to represent the knowledge in robot task planning. Then, the paper proposes a method to evaluate the scalability and responsiveness of RTPO. Finally, the corresponding task planning algorithm is designed based on RTPO, and then the paper conducts experiments on the basis of the real robot TurtleBot3 to verify the usability of RTPO. The experimental results demonstrate that RTPO has good performance in scalability and responsiveness, and the robot can achieve given high-level tasks based on RTPO.

scholarly journals Inter-player variability in game patterns in high-level women's volleyball: A study with Outside Hitters (Near vs. Away) using Social Network Analysis. [Variabilidad entre jugadores en los patrones de juego en el voleibol femenino de alto nivel: Un estudio con bateadores externos (Cerca versus Lejos) usando Análisis de Redes Sociales].

2021 ◽  
Vol 17 (65) ◽  
pp. 234-250
Author(s):  
João Bernardo Martins ◽  
◽  
Isabel Mesquita ◽  
Ademilson Mendes ◽  
Letícia Santos ◽  
...  
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Team Sports ◽  
Eigenvector Centrality ◽  
Redes Sociales ◽  
Functional Roles ◽  
High Level ◽  
Women's Volleyball ◽  
After Error

A wide body of research on team sports has focused on positional status based differences, providing information on inter-player variability according to the functional roles within the game. However, research addressing inter-player variability within the same positional/function status is scarce. The present article presents an analysis of inter-player variability within the same positional status during critical moments, in high-level women's volleyball, using Social Network Analysis. Attack actions of the outside hitters near (OHN) and away (OHA) from the setter were analysed in ten matches from the 2019 Volleyball Nations League Finals (268 plays). Two independent Eigenvector Centrality networks were created, one for OHN and another for OHA. Main results: (a) in side-out with ideal setting conditions, the OHA used more tips and exploration of the block than the OHN; under non-ideal setting conditions, the OHN had slower attack tempos than the OHA; (b) OHA used tip and directed attacks after error situations while OHN was typically not requested after error situations; (c) in transition, OHN typically attacked after having performed a previous action, performing a dual task within each ball possession, while OHA only attacked when there was no prior action; (d) there were also inter-positional similarities, with both OHN and OHA preferring a strong attack in ideal conditions during KI and KIV, and slower tempos in transition in non-ideal conditions. Conclusions: Even within the same positional status, there seems to be subtle, but relevant inter-player variability. Consequently, coaches should devote careful attention when assigning players to positional.

scholarly journals Deep Learning to Detect Skin Cancer using Google Colab

2019 ◽  
Vol 8 (6) ◽  
pp. 2176-2183 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Skin Cancer ◽  
Domain Knowledge ◽  
Error Rates ◽  
Data Set ◽  
Training Models ◽  
Learning Machine ◽  
High Level

Different mathematical models, Artificial Intelligence approach and Past recorded data set is combined to formulate Machine Learning. Machine Learning uses different learning algorithms for different types of data and has been classified into three types. The advantage of this learning is that it uses Artificial Neural Network and based on the error rates, it adjusts the weights to improve itself in further epochs. But, Machine Learning works well only when the features are defined accurately. Deciding which feature to select needs good domain knowledge which makes Machine Learning developer dependable. The lack of domain knowledge affects the performance. This dependency inspired the invention of Deep Learning. Deep Learning can detect features through self-training models and is able to give better results compared to using Artificial Intelligence or Machine Learning. It uses different functions like ReLU, Gradient Descend and Optimizers, which makes it the best thing available so far. To efficiently apply such optimizers, one should have the knowledge of mathematical computations and convolutions running behind the layers. It also uses different pooling layers to get the features. But these Modern Approaches need high level of computation which requires CPU and GPUs. In case, if, such high computational power, if hardware is not available then one can use Google Colaboratory framework. The Deep Learning Approach is proven to improve the skin cancer detection as demonstrated in this paper. The paper also aims to provide the circumstantial knowledge to the reader of various practices mentioned above.

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