Deep learning in sport video analysis: A review

• Main Authors: Rangasamy, Keerthana, As’ari, Muhammad Amir, Rahmad, Nur Azmina, Ghazali, Nurul Fathiah, Ismail, Saharudin
• Format: Article
• Language: English
• Published: Universitas Ahmad Dahlan 2020
• Subjects: QM Human anatomy
• Online Access: http://eprints.utm.my/id/eprint/91070/1/MuhammadAmirAsAri2020_DeepLearningInSportVideoAnalysis.pdf; http://eprints.utm.my/id/eprint/91070/; http://dx.doi.org/10.12928/TELKOMNIKA.V18I4.14730
• Institution: Universiti Teknologi Malaysia

Sport is a competitive field, where it is an element of measurement for a countries development. Due to this reason, sport analysis has become one of the major contribution in analysing and improving the performance level of an athlete. Video-based modality has become a crucial tool used in sport analysis by coaches and performance analysis. There were wide variety of techniques used in sport video analysis. The main purpose of this review paper is to compare and update review between traditional handcrafted approach and deep learning approach in sport video analysis based on human activity recognition, overview of recent study in video based human activity recognition in sport analysis and finally concluded with future potential direction in sport video analysis.