基于多尺度CLG光流法的多目标检测方法

多目标检测是计算机视觉中一个重要的研究方向，如何才能准确地检测目标在军事、生活、工业等方面都有着极为重要的研究意义。针对传统光流法在目标检测中对噪声鲁棒性较差的问题，为了提高算法对噪声的鲁棒性，本文将CLG光流法与多尺度的思想相结合。多尺度的主要思想是建立一个图像金字塔，由粗到精地计算光流向量，并在此基础上将L2范数的平方改为L1范数。实验结果表明，改进后的多尺度CLG光流算法相对于原始CLG光流算法有更好的整体效果，说明多尺度CLG光流算法对噪声具有更好的鲁棒性，可以较好地对图像进行光流估计。

Multi-target Detection Method Based on Multi-scale CLG Optical Flow Method

Multi-target detection is an important research direction in computer vision. How to accurately detect targets in military, life, industry and other aspects have very important research significance. Aiming at the poor robustness to noise of traditional optical flow methods in target detection, in order to improve the robustness of the algorithm against noise, the CLG optical flow method is combined with the idea of multi-scale. The main idea of multi-scale is to build an image pyramid, calculate the optical flow vector from coarse to fine, and change the square of L2 norm to L1 norm. Experimental results show that the improved multi-scale CLG optical flow algorithm has better overall performance than the original CLG optical flow algorithm, indicating that the multi-scale CLG optical flow algorithm has better robustness to noise, and can better estimate the optical flow of images.