LAUSR

Most existing researches for semantic analysis of soccer videos benefit from special approaches to bridge the semantic gap between low-level features and high-level events using a hierarchical structure. In this paper, we propose a novel data-driven model for automatic recognition of important events in soccer broadcast videos based on the analysis of spatio-temporal local features of video frames. Our presented algorithm explores the local visual content of video frames by focusing on spatial and temporal learned features in a low-dimensional transformed sparse space. The proposed algorithm, without using mid-level futures, dynamically extracts the most informative semantic concepts/features and improves the generality of the system. The dictionary learning process plays an important role in sparse coding and sparse representation-based event classification. In this paper, we present a novel dictionary learning method, which calculates several category-specific dictionaries by training the detected shots of various view categories. In order to evaluate the feasibility and effectiveness of the proposed algorithm, an extensive experimental investigation is conducted for the analysis, detection, and classification of soccer events on a large collection of video data. Experimental results indicate that our approach outperforms the state-of-the-art methods and demonstrate the effectiveness of the proposed approach.