基于张量低秩恢复和块稀疏表示的运动显著性目标提取

Motion Saliency Extraction via Tensor Based Low-rank Recovery and Block-sparse Representation

针对视频的高维结构特性,采用张量表征并将运动显著性目标提取转化为基于低秩张量恢复和块稀疏表示问题.首先根据背景张量的低秩性和运动目标的稀疏性,利用加速近端梯度张量恢复方法分别重建出RGB颜色通道中三维视频张量的低秩部分与稀疏部分,初步实现背景与运动目标的粗略分离;其次组合三颜色通道稀疏张量并转化为按照帧数展开的二维矩阵,进一步通过矩阵恢复的方法去除动态背景产生的小稀疏块干扰;最后通过自适应阈值法选择运动目标稀疏块掩模并对存在的空洞进行填充补偿,以达到重构出完整前景运动目标的目的.相对于常用方法,该方法从张量模型角度解决运动目标提取问题,较大程度地保护了视频序列的原始空间结构,不仅能够降低运动目标提取区域出现的漏检问题,而且可以很好地去除动态背景所带来的干扰.实验结果表明,该方法对运动目标提取的准确度较高,鲁棒性较强.

According to the high dimensional structure of the video modality, this paper formulates the motion saliency extraction as the tensor based low-rank recovery and block-sparse representation problem. First, the proposed approach utilizes the accelerated proximal gradient based tensor recovery to reconstruct the low-rank and sparse sensors of RGB color channels, through which the rough motion saliency can be initially separated from the background. Then, the sparse tensors of the three color channels are grouped together and unfolded into the matrix in terms of the frame number. Subsequently, the matrix recovery method is further employed to process this unfolded matrix such that the small but irrelevant sparse components can be removed. Finally, the adaptive threshold method is utilized to select the block-sparse mask with respected to motion saliency and the holes within the mask is simultaneously filled, whereby the motion saliency can be well extracted. Comparing with traditional methods, the proposed approach utilizing the tensor model to preserve the spatial structure of the video modality, not only can reduce the missing detection problem, but also isable to remove the interferences of dynamic background. The experimental results have shown the promising performances.