基于区域属性特征和多核支持向量机的运动视频分类

为了提高视频搜索平台中海量运动视频数据的分类精度,提出了一种基于区域属性特征和多核支持向量机的运动视频分类方法。首先,利用区域分块策略对输入视频进行初步处理,并提取颜色特征以便快速获取视频中的关键帧。然后,根据每个区域中的运动属性、纹理属性等因素来提取视频的主要特征。最后,采用权重的方式将局部和全局的核函数进行线性相加,组成多核支持向量机实现视频分类。在6种类型运动视频构成的数据集上进行了分类测试。实验结果表明,相比于其他类似的支持向量机方法,提出方法在查全率、查准率和F1三个分类评估指标上均有一定提高,平均分类F1指标达到92%。

大数据分析技术的运动视频内容分类算法

针对当前运动视频分类方法存在分类误差大,分类时间长等问题,以提高运动视频分类效果为目标,设计基于大数据分析技术的运动视频内容分类方法。首先,对当前运动视频分类的研究进展进行分析,同时指出各种方法的局限性;然后,采集运动视频分类视频,并引入数据分析技术,建立运动视频分类方法模型;最后进行了运动视频分类方法的性能测试。测试结果表明,相比于其他运动视频分类方法,该方法的运动视频分类精度高,具有更高的实际应用价值。

A robust system for real-time pedestrian detection and tracking

A real-time pedestrian detection and tracking system using a single video camera was developed to monitor pedestrians. This system contained six modules: video flow capture, pre-processing, movement detection, shadow removal, tracking, and object classification. The Gaussian mixture model was utilized to extract the moving object from an image sequence segmented by the mean-shift technique in the pre-processing module. Shadow removal was used to alleviate the negative impact of the shadow to the detected objects. A model-free method was adopted to identify pedestrians. The maximum and minimum integration methods were developed to integrate multiple cues into the mean-shift algorithm and the initial tracking iteration with the competent integrated probability distribution map for object tracking. A simple but effective algorithm was proposed to handle full occlusion cases. The system was tested using real traffic videos from different sites. The results of the test confirm that the system is reliable and has an overall accuracy of over 85%.