基于独立分量分析的运动目标检测算法中对通道数选择和观测向量生成方式的实验和分析

现有基于独立分量分析(ICA)的运动目标检测算法大多采用单一的观测向量生成方式和2通道数据进行检测,使得现有算法难以获得更加完整精确的目标形态.该文在传统独立分量分析算法的基础上引入4种不同的观测向量生成方式并使用更多通道数据进行实验,以此更广泛地涵盖运动目标的运动特性并为提取前景提供更多有效信息,使该算法能有效应对缓慢移动和低区分性目标.多场景下的量化实验分析表明,更多通道数据的使用以及4种观测向量生成方式的综合在合理的误检率代价下使算法达到了更高的检测正确率.

Experiments and Analysis on Observation Vector Generation and Channel Number Selection in Motion Detection Algorithm Based on Independent Component Analysis

Most of the existing Independent Component Analysis (ICA) based motion detection algorithms use a single observation vector generation method and two-channel data for motion detection, which make the traditional algorithms unable to obtain a more complete and accurate state of the moving objects. In this paper, four different observation vector generation methods are proposed and larger channel numbers are introduced into traditional ICA. The motion characteristics of the moving objects are covered more widely and more information for foreground extraction is obtained from the multi-channel data. These improvements make ICA be able to deal with indistinguishable and slowly moving objects. The quantitative evaluation from different experiments shows that the multi-channel data and the combination of four observation vector generation methods enable ICA to achieve a better performance with a reasonable cost of tiny increase on false alarms.