Automatic soccer video analysis and summarization

Abstract

We propose a fully automatic and computationally efficient framework for analysis and summarization of soccer videos using cinematic and object-based features. The proposed framework includes some novel low-level processing algorithms, such as dominant color region detection, robust shot boundary detection, and shot classification, as well as some higher-level algorithms for goal detection, referee detection, and penalty-box detection. The system can output three types of summaries: i) all slow-motion segments in a game; ii) all goals in a game; iii) slow-motion segments classified according to object-based features. The first two types of summaries are based on cinematic features only for speedy processing, while the summaries of the last type contain higher-level semantics. The proposed framework is efficient, effective, and robust. It is efficient in the sense that there is no need to compute object-based features when cinematic features are sufficient for the detection of certain events, e.g., goals in soccer. It is effective in the sense that the framework can also employ object-based features when needed to increase accuracy (at the expense of more computation). The efficiency, effectiveness, and robustness of the proposed framework are demonstrated over a large data set, consisting of more than 13 hours of soccer video, captured in different countries and under different conditions.

Semantic Based Sport Video Browsing

“…The accumulative histograms of the HSI components are constructed from training frames of global views randomly selected form wide range of soccer videos and the initial dominant color are determined from the color histogram [36]. 2) Initial dominant color region determination.…”

“…2) Initial dominant color region determination. The initial dominant color and the cylindrical www.intechopen.com Semantic Based Sport Video Browsing 141 metric [36] are then utilized to classify each pixel of current frame into dominant color or non-dominant color. 3) Adaptive dominant color refinement.…”

Scalable gastroscopic video summarization via similar-inhibition dictionary selection

“…For video summarization [6][7][8][9][10], most state-of-the-art methods mainly focus on the summarization of structured videos, such as sports, cartoons or surveillance videos. In comparison, the automatic summarization of unstructured data, e.g., gastroscopic videos, is much more challenging.…”

Video Summarization: Survey on Event Detection and Summarization in Soccer Videos

“…Video summarization refers to creating a summary of a video that addresses three main points. (1) The video summary should contain scenes and events not only as short as possible from the video but also the most important one. For example, in a soccer game, the summary must contain goals, fouls, shot boundaries, goal attempt, and some other important scenes.…”