Abstract
With the rapid technological advances in sports, the number of athletics increases gradually. For sports professionals, it is obligatory to oversee and explore the athletics pose in athletes’ training. Key frame extraction of training videos plays a significant role to ease the analysis of sport training videos. This paper develops a sports actions’ classification system for accurately classifying athlete’s actions. The key video frames are extracted from the sports training video to highlight the distinct actions in sports training. Subsequently, a fully convolutional network (FCN) is used to extract the region of interest (ROI) pose detection of frames followed by the application of a convolution neural network (CNN) to estimate the pose probability of each frame. Moreover, a distinct key frame extraction approach is established to extract the key frames considering neighboring frames’ probability differences. The experimental results determine that the proposed method showed better performance and can recognize the athlete’s posture with an average classification rate of 98%. The experimental results and analysis validate that the proposed key frame extraction method outperforms its counterparts in key pose probability estimation and key pose extraction.
Highlights
With the advent of artificial intelligence, performance analysis in sport has undergone significant changes in recent years
fully convolutional network (FCN) is applied to get the region of interest (ROI) for a more accurate pose detection of frames followed by the application of a convolution neural network (CNN) to estimate the pose probability of individual frames
Prominent features are generated for classification, so they are broadly used for object detection and classification of images
Summary
With the advent of artificial intelligence, performance analysis in sport has undergone significant changes in recent years. Intelligent sports action recognition methods are developed to provide objective analysis and evaluation in sport and improve the accuracy of sports performance analysis and validate the efficiency of training programs. Sports activities recorded through a computer vision system can be used for athlete action detection, movement analysis, and pose estimation [3]. In sports video analysis and processing for action recognition, pertinent and basic information extraction is a mandatory task. During the analysis of videos, the major steps are scene segmentation, detection of shot margin, and key frame extraction [8, 9]. We propose an effective method for the extraction of a key frame from athlete sports video, which is accurate, fast, and efficient.
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