基于特征融合和L-M算法的车辆重识别方法

车辆重识别是在视频监控系统中, 匹配不同外界条件下拍摄的同一车辆目标的技术。针对车辆重识别时不同摄像机中同一车辆的图像差异较大，单一特征难以稳定地描述图像的问题，采用多种特征融合实现车辆特征的提取，该方法将车辆图片的HSV特征和LBP特征进行融合，并对融合特征矩阵进行奇异值分解，提取特征值。针对重识别模型训练时传统BP算法收敛速度慢，精度不高的问题，采用Levenberg-Marguardt自适应调整算法优化BP神经网络。实验结果表明,该方法在车辆的同一性识别方面的识别率达到975%,且对光照变化、视角变化都具有较好的鲁棒性。

Vehicle Re-identification Method Based on Feature Fusion and L-M Algorithm

Vehicle re-identification is a technique for matching the same vehicle target under different conditions in a video surveillance system. In the case of vehicle re-identification, the images of the same vehicle in different cameras are different, and the single feature is difficult to describe the image stably, using a variety of features fusion characteristics of the vehicle, the method combines HSV and LBP features of vehicle images, and the fusion feature matrix singular value decomposition, feature extraction. In view of the slow convergence rate and low precision of traditional BP algorithm, the Levenberg-Marguardt adaptive adjustment algorithm is adopted to optimize the BP neural network. The experimental results show that the recognition rate of the method is 97.5%, and it is robust to illumination changes and angle changes.