基于增强多流形学习的监控视频追踪算法

提出了一种多流形局部线性嵌入的流形学习算法,为每个类的流形学习过程设计了一种监督的近邻点选择方法,将流形-流形距离作为度量指标,搜索最优的低维空间.在视频追踪算法中对外部数据库进行图像训练预处理,为人脸检测建立级联分类器,利用均值粒子滤波器结合跟踪校正策略对人脸图像实时跟踪,采用多流形训练的结果从视频流的人脸集中检测出追踪的目标人脸.仿真实验结果表明本算法对不同的数据集均获得了较高的检测率与较高的计算效率.

A Tracking Algorithm of Surveillance Video Based on Enhanced Multi-Manifold Learning

The traditional manifold learning algorithms cannot preserve the structure of individual manifolds during multi-class-multi-manifold learning problems, and have obvious influence to the performance of multi-classes identification problems, thus a manifold learning algorithm of multi-manifold locally linear embedding has been proposed. A supervised neighborhood selection method has been designed by this multi-manifold learning algorithm for the manifold learning procedure of each class, and the distances of manifold to manifold have been set as the metric to search the optimal low dimensional space. Image training preprocess of external database has been realized during the video tracking algorithm, the cascade classifier has been constructed for face detection, and the mean particle filter combined with tracking correction strategy has been adopted for real-time tracking of face images, the results of multi-manifold learning training are used to identify the target faces from the face set of video stream. Simulation experiments are implemented based on the large scale video datasets, the results show that the proposed algorithm realizes a high detection accuracy and a high computational efficiency to different video datasets.