基于隐马尔科夫模型的足球精彩视频事件检测方法研究

Research on Detection Method of Football Highlight Video Events Based on Hidden Markov Model

摘要: 随着足球赛事的广泛传播与数据分析需求的增长，实现足球视频事件的自动检测意义重大。本研究聚焦于运用隐马尔可夫模型(HMM)进行足球视频事件检测。首先，从足球视频中提取多维度特征，本文设计了分层特征提取方案。在颜色特征层面，HSV空间直方图主成分分析(PCA)有效表征球场区域的稳定性变化；在纹理特征层面，联合能量(角二阶矩)、熵、对比度以及相关性等统计量，捕捉球员动作引发的局部结构波动；在运动特征层面，基于Farneback稠密光流计算平均幅值与方向直方图主成分，量化全局运动强度与方向趋势。三类特征共同构成10维观测向量，兼顾静态场景与动态运动信息。随后，构建HMM模型，将进球、射门、罚牌、扑救四类足球事件定义为隐藏状态集$S=\left\{s_{goal},s_{shot},s_{card},s_{save}\right\}$ ，通过Baum-Welch算法优化状态转移矩阵A与观测概率分布B。其中，观测概率采用高斯混合模型(GMM)建模，以解决特征分布的多模态问题。在解码阶段，结合Viterbi算法与状态驻留时间约束，生成最优事件序列，有效抑制瞬时误检。

Abstract: With the extensive spread of football matches and the increasing demand for data analysis, it is of great significance to realize the automatic detection of football video events. This study focuses on football video event detection using the Hidden Markov Model (HMM). First, to extract multi-dimensional features from football videos, this paper designs a hierarchical feature extraction scheme. At the level of color characteristics, the Principal Component Analysis (PCA) of HSV space histogram effectively represents the stability change of court area; at the level of texture characteristics, the combined statistics of energy (second moment), entropy, contrast and correlation capture the local structural fluctuations caused by players’ movements; at the level of motion characteristics, the average amplitude and direction principal components are calculated based on the Farneback dense optical flow to quantify the global motion intensity and direction trends. Three types of features together constitute a 10-dimensional observation vector, taking into account both static scene and dynamic motion information. Subsequently, the HMM model was constructed, defining the four types of football events of goal, goal, shot, penalty and save as the hidden state set $S=\left\{s_{goal},s_{shot},s_{card},s_{save}\right\}$ , and the state transition matrix A and the observed probability distribution B were optimized by the Baum-Welch algorithm. The observation probability is modeled by Gaussian Mixed Model (GMM) to solve the multi-modal problem of feature distribution. In the decoding phase, Viterbi algorithm is combined with state residing time constraints to generate an optimal sequence of events to effectively suppress transient misdetection.