Color has been widely used in sports video analysis. Previous techniques, however, require color models from prior information or user interaction, and do not address the problem of how to automatically form color models from a video in an arbitrary sports setting. In this paper, we propose an automatic technique for extracting color models of the playing surface and the team uniforms, which can be used in higher-level processes such as tracking and recognition. Unlike most previous methods, our approach is capable of handling multi-colored patterns like striped uniforms and playing fields. Multiple forms of color processing are used to analyze video frame content, which are then used iteratively to refine the color models. The results of our color modeling technique have been applied to shot classification, and experiments on videos of different sports have verified our approach.

<abstract> <p>The utilization of intelligent computing in digital teaching quality evaluation has been a practical demand in smart cities. Currently, related research works can be categorized into two types: textual data-based approaches and visual data-based approaches. Due to the gap between their different formats and modalities, it remains very challenging to integrate them together when conducting digital teaching quality evaluation. In fact, the two types of information can both reflect distinguished knowledge from their own perspectives. To bridge this gap, this paper proposes a textual and visual features-jointly driven hybrid intelligent system for digital teaching quality evaluation. Visual features are extracted with the use of a multiscale convolution neural network by introducing receptive fields with different sizes. Textual features serve as the auxiliary contents for major visual features, and are extracted using a recurrent neural network. At last, we implement the proposed method through some simulation experiments to evaluate its practical running performance, and a real-world dataset collected from teaching activities is employed for this purpose. We obtain some groups of experimental results, which reveal that the hybrid intelligent system developed by this paper can bring more than 10% improvement of efficiency towards digital teaching quality evaluation.</p> </abstract>