面向特征的ICA和HP滤波实现视频异常事件检测

Video unusual event detection using feature-orientedICA and HP filter

针对室外环境下光照亮度变化、阴影和树木遮挡等问题,对利用隐马尔可夫模型进行视频异常事件检测的影响,提出基于独立分量分析(ICA)和HP(Hodrick-Prescott)滤波器的隐马尔可夫模型视频异常事件检测方法。该方法首先利用ICA构造正常视频的特征子空间,将图像序列投影到特征子空间上得到投影序列,实现数据降维;然后利用HP滤波器滤除投影序列中环境变化引起的趋势分量;最终克服不利的环境因素,有效改善隐马尔可夫模型的视频异常事件检测性能。机动车辆禁行路段视频的检测实验表明,该方法能够在复杂的室外环境下较好地检测出异常事件。

Many factors in outdoor environment, such as light, shadows and trees blocks etc. will affect the video unusual event detection based on the hidden Markov model（HMM）. So using the independent component analysis （ICA） and HP （Hodrick-Prescott） filter, a new HMM video unusual event detection method is proposed. This method first employed ICA to construct a normal video feature subspace, and projected the image sequence into this subspace to achieve data reduction. Then the trend component in feature sequence caused by the environment factors is cancelled by the HP filter. So to overcome the adverse environmental factors, the effect of the proposed method is improved. Using the video data in a forbidden road for motor vehicles, the test result shows the proposed method can detect the video unusual events effectively in the complex outdoor environments.