一种基于MI-Simba算法的香烟烟雾识别方法

摘要

To overcome the uncertainty of smoke characteristics caused by the environment background, inhibit the redundancy between video smoke features, and improve the recognition rate simultaneously, a MI-Simba algorithm combining mutual information and simba for recognizing cigarette smoke in indoor videos is proposed. Firstly, the statistic feature, color layout feature and dynamic feature of cigarette smoke are obtained by the method of video feature extraction, and then the initial feature vector is established. Secondly, the feature vector is updated automatically by MI-Simba, and the optimal feature combination in this environment is established. Then a transductive support vector machine(TSVM) is used for classification and recognition. Finally, the recognition rate and sensitivity are computed on the self-built video sequence database by 5-fold cross validation. The experimental results demonstrate the validity and superiority of the proposed algorithm compared with other algorithms.%香烟烟雾对环境条件敏感以及多特征间存在冗余，都导致无法在视频监控中准确进行烟雾识别，因此提出一种高维互信息与Simba特征加权相结合的算法( MI-Simba)。首先采用视频特征提取方法获取烟雾统计度量特征、颜色布局特征和动态特征，构建初始特征向量；然后利用MI-Simba算法进行自动更新，构建该环境下最优特征组合；最后采用直推式支持向量机进行分类识别。针对室内和楼宇内场景，自建封闭空间吸烟视频数据集，采用5倍交叉策略进行比较验证，实验结果验证该算法在识别率和灵敏度两方面的有效性和优越性。