Learning an event-oriented and discriminative dictionary based on an adaptive label-consistent K-SVD method for event detection in soccer videos

Abstract In this paper, we formulate the soccer video event detection task as a sparse representation problem by learning a supervised, discriminative and event-oriented dictionary based on learned weighted local features. To this end, we present a novel framework based on two ideas: First, we propose an approach for computing the representativeness of each video frame for each soccer event. Second, we propose an Adaptive Label-Consistent K-SVD (ALC-KSVD) algorithm to learn an event-oriented and discriminative dictionary based on the computed representativeness of frames to transfer video frames to a sparse space. To improve discrimination among frames of different events, we proposed a weighting method to identify local features that are more representative in each event category. Next, the representativeness score of each frame is calculated by aggregating the weighted local features within each frame. The calculated representativeness score of each frame indicates its belonging degree to each event. The representativeness score matrix, being a discriminative term, is combined with the reconstruction error to form an objective function to improve the discrimination ability in the sparse representation during the dictionary learning process. The obtained objective function is efficiently and optimally solved by the K-SVD algorithm. The representativeness score matrix, which is automatically calculated based on the training samples, defines an adaptive correspondence between the dictionary atoms and the labels of the frames. We demonstrate the effectiveness of the proposed framework on the detection and classification of several soccer events based on an extensive experimental investigation that was conducted using a large collection of video data. The experimental results indicate that our approach maintains good classification performance and outperforms the state-of-the-art methods.