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Understanding and optimizing the complex action of dynamic soccer players is vital for various applications in sports analytics and training. This study focuses on integrating multi-channel perceptual visual attributes to describe images of soccer games, which often include complex spatial arrangements. Central to our approach is the development of a deep multi-layer architecture tailored to precisely analyze soccer players’ movements, which are subsequently optimized by a probabilistic model. We utilize the BING objectness metric to efficiently locate representative patches within these soccer-relevant sceneries, such as players, the ball, or goal areasąłacross different scales. Following the localization process, both multi-task and manifold-guided mathematical models are formulated to select visual features from these key patches adaptively. To emulate the observational skills of soccer coaches and players in recognizing crucial moments of play, we introduce a technique known as locality-preserved and interactive active detection (LIAD). This method systematically generates gaze dynamic sequence (GDS) for each soccer scene, enhancing our understanding of soccer tactical formations and player positioning. The advantages of LIAD include its ability to: 1) preserve the local context of diverse game situations effectively, and 2) engage system designer in an active selection process driven by interactive feedback. By implementing LIAD, we methodically construct a GDS for each soccer scene and compute its deep attributes, which are then integrated into a probabilistic Gaussian mixture model (GMM) for optimize each soccer scenery. Our methodology’s efficacy is confirmed through comprehensive empirical studies, demonstrating its effectiveness in soccer training education. Notably, our derived GDSs have shown exceptional discriminative capability within our collected data set of educational soccer images, providing valuable insights for training and tactical analysis. |
