欧拉弹力模型的视频运动目标自适应图切检测

传统的视频运动目标图切检测算法基于低阶马尔科夫随机场,能量函数的低阶近似无法准确描述图像像素的空间相关性,导致图切检测结果过度平滑。本文提出一种基于高阶欧拉弹力模型的图切检测算法,利用欧拉弹性模型优化目标边界曲线和修正能量函数的低阶近似。算法通过利用前一帧图像的检测结果,对当前帧图像运动目标像素点数和前景背景邻接像素对数进行卡尔曼预测,并不断自适应调整当前帧的图像模型参数,实现了视频运动目标的连续全局优化检测。实验结果验证了欧拉弹力模型在视频运动目标检测中的有效性,其检测结果能够更好地满足人的视觉效果。

Adaptive Graph-cut Algorithm of Video Moving Object Detection Based on Euler's Elastica Model

The traditional graph-cut algorithm of video moving objects detection is based on the low-order Markov Random Field (MRF). Because of the low order approximation of the energy function, the detected moving objects will be over-smoothing. In this paper, an adaptive graph-cut algorithm based on Euler’s elastica model is proposed, which uses Euler’s elastica model to optimize the objects boundary and to amend the low-order approximation of the energy function. The proposed algorithm can continuously update the model parameters of current frame by Kalman prediction which estimates the number of moving objects pixels and objectives-background pixel-pairs. So the proposed algorithm can detect video moving objects in a continuous optimal mode. Experimental results show that the proposed method can effectively and stably detect moving objects, and the detection results can better meet the requirements of person's visual effects.