Recognition of basketball referee signals from videos using Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM)

Žemgulys, Julius; Raudonis, Vidas; Maskeliūnas, Rytis; Damaševičius, Robertas

doi:10.1016/j.procs.2018.04.095

Cited by 39 publications

(20 citation statements)

References 6 publications

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“…Ada 4 teknik dasar dalam permainan bola basket, yaitu penguasaan bola (ball handling), mengoper dan menangkap bola (passing dan catching), memantulkan bola ke lantai (dribbling), dan tembakan (Shooting) (Ranisavljev, Mandic, Cosic, Blagojevic, & Dopsaj, 2020). Permainan bola basket diatur oleh dua orang wasit yang bertugas sebagai referee dan umpire (Žemgulys, Raudonis, & Maskeliūnas, 2018). Atlet bola basket perlu mengumpulkan banyak informasi, terutama visual, dengan cepat dari lingkungan untuk melaksanakan tugas motorik yang sesuai dengan rangsangan yang datang (Jim, David, & Garc, 2020).…”

Section: Pendahuluanunclassified

Exercise Motivation Levels on Student Activity Unit (UKM Basketball) at Universitas Djuanda

Yudho

Nugroho

2021

INSPIREE

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Tujuan penelitian. Penelitian ini bertujuan untuk mengetahui seberapa tinggi tingkat motivasi latihan atlet ketika melaksanakan latihan. Latar belakang dari pada penelitian ini adalah adanya ketidak konsistenan tingkatan prestasi yang ditunjukkan oleh Unit Kegiatan Mahasiswa Bola Basket Universitas Djuanda Bogor. Metode penelitian. Penelitian ini dilaksanakan dengan pendekatan kuantitatif, melalui metode deskriptif dengan menggunakan metode survei terhadap anggota tim Bola Basket yang terdiri dari 20 orang. Data berupa nilai angket pada skala likert di olah menggunakan statistik sederhana SPSS Versi 26 untuk mengetahui nilai statistik deskriptif sebagai hasil akhir penelitian. Hasil dan pembahasan. Hasil penelitian menunjukan bahwa tingkatan motivasi atlet yang mengikuti latihan pada Unit Kegiatan Mahasiswa (UKM) bola basket di Universitas Djuanda Bogor berada pada mayoritas sedang. Hasil tersebut berdasarkan pertimbangan frekuensi terbanyak berada pada kriteria sedang 40%. Motivasi atlet pada kriteria sangat tinggi sebesar 5%, tinggi 20%, kurang 20% dan sangat kurang 15%. Kesimpulan. Persentase motivasi yang terungkap dari penelitian ini memiliki di dominasi kategori sedang, dengan presentase yang kurang lebih besar presentase nya dibanding presentase tinggi 25%<35%. Hal ini dapat menjadi bahan pertimbangan untuk dapat mengevaluasi program latihan psikologis dan mental training terhadap para atlit klub bola basket tersebut untuk pencapaian prestasi yang lebih baik lagi.

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Section: Pendahuluanunclassified

Exercise Motivation Levels on Student Activity Unit (UKM Basketball) at Universitas Djuanda

Yudho

Nugroho

2021

INSPIREE

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“…From the perspective of sports cognition, the decision-making of basketball players directly affects the tactical performance and game result of the whole team (Žemgulys et al, 2018 ; Kim and Lee, 2020 ). Therefore, players are required to be able to capture the basketball target in real time and complete relevant information processing when make decisions.…”

Section: Methodsmentioning

confidence: 99%

Adoption of Machine Learning Algorithm-Based Intelligent Basketball Training Robot in Athlete Injury Prevention

Tang

2021

Front. Neurorobot.

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In order to effectively prevent sports injuries caused by collisions in basketball training, realize efficient shooting, and reduce collisions, the machine learning algorithm was applied to intelligent robot for path planning in this study. First of all, combined with the basketball motion trajectory model, the sport recognition in basketball training was analyzed. Second, the mathematical model of the basketball motion trajectory of the shooting motion was established, and the factors affecting the shooting were analyzed. Thirdly, on this basis, the machine learning-based improved Q-Learning algorithm was proposed, the path planning of the moving robot was realized, and the obstacle avoidance behavior was accomplished effectively. In the path planning, the principle of fuzzy controller was applied, and the obstacle ultrasonic signals acquired around the robot were taken as input to effectively avoid obstacles. Finally, the robot was able to approach the target point while avoiding obstacles. The results of simulation experiment show that the obstacle avoidance path obtained by the improved Q-Learning algorithm is flatter, indicating that the algorithm is more suitable for the obstacle avoidance of the robot. Besides, it only takes about 250 s for the robot to find the obstacle avoidance path to the target state for the first time, which is far lower than the 700 s of the previous original algorithm. As a result, the fuzzy controller applied to the basketball robot can effectively avoid the obstacles in the robot movement process, and the motion trajectory curve obtained is relatively smooth. Therefore, the proposed machine learning algorithm has favorable obstacle avoidance effect when it is applied to path planning in basketball training, and can effectively prevent sports injuries in basketball activities.

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“…[ 29 ]. The Myo Arm band was used to collect gestures data for Hand Cricket between two persons [ 30 ].…”

Section: Background and Related Workmentioning

confidence: 99%

Wearable-Sensors-Based Platform for Gesture Recognition of Autism Spectrum Disorder Children Using Machine Learning Algorithms

Siddiqui

Ullah

Iqbal

et al. 2021

Sensors

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Autistic people face many challenges in various aspects of daily life such as social skills, repetitive behaviors, speech, and verbal communication. They feel hesitant to talk with others. The signs of autism vary from one individual to another, with a range from mild to severe. Autistic children use fewer communicative gestures compared with typically developing children (TD). With time, the parents may learn their gestures and understand what is occurring in their child’s mind. However, it is difficult for other people to understand their gestures. In this paper, we propose a wearable-sensors-based platform to recognize autistic gestures using various classification techniques. The proposed system defines, monitors, and classifies the gestures of the individuals. We propose using wearable sensors that transmit their data using a Bluetooth interface to a data acquisition and classification server. A dataset of 24 gestures is created by 10 autistic children performing each gesture about 10 times. Time- and frequency-domain features are extracted from the sensors’ data, which are classified using k-nearest neighbor (KNN), decision tree, neural network, and random forest models. The main objective of this work is to develop a wearable-sensor-based IoT platform for gesture recognition in children with autism spectrum disorder (ASD). We achieve an accuracy of about 91% with most of the classifiers using dataset cross-validation and leave-one-person-out cross-validation.

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Recognition of basketball referee signals from videos using Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM)

Cited by 39 publications

References 6 publications

Exercise Motivation Levels on Student Activity Unit (UKM Basketball) at Universitas Djuanda

Exercise Motivation Levels on Student Activity Unit (UKM Basketball) at Universitas Djuanda

Adoption of Machine Learning Algorithm-Based Intelligent Basketball Training Robot in Athlete Injury Prevention

Wearable-Sensors-Based Platform for Gesture Recognition of Autism Spectrum Disorder Children Using Machine Learning Algorithms

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