一种融合预测过采样的运动目标检测算法

Prediction Based Over-sampling for Moving Object Detection

为了改善运动目标检测的精度,提出了一种融合了预测过采样的运动目标检测新方法。首先,基于二维傅里叶变换预测当前帧的目标形状并计算形状相似度;然后,从历史检测结果中选择一定数量的参考帧,使用光流法跟踪目标像素点在参考帧与当前帧之间的运动轨迹,并以像素点轨迹为参考在采样区间执行稠密过采样;最后,基于过采样样本构造前景模型,并在图分割框架内联合使用前景背景模型实现目标检测。在公共数据与自采数据集上对所提方法进行了实验验证,结果表明,相对于经典的运动目标检测算法,所提方法能够有效提高检测精度。

Prediction based over-sampling is proposed for moving object detection.First,2D Fourier transform is used to select some historical foreground frames as references of over-sampling,where the selection rule is shape sim ilarity.Then foreground pixels are tracked from reference frames to the current frame with optical flow method.Next,dense over-sampling is conducted to generate new samples which are then used for improved foreground modeling.F inally,the foreground model is used in conjunction with another background model for graph cut based classification.Comparison between the presented method and state-of the-arts shows that the generated samples are representative and the proposed algorithm is effective.