中文摘要：

针对光照、姿态变化等情况下的目标稳定跟踪问题，提出了一种基于梯度方向直方图和子流形的目标跟踪方法。首先对目标区域进行划分，将各子区域的梯度方向直方图组合作为目标初始特征描述；然后通过局部保留投影将初始特征投影到子流形得到低维特征描述。在特征提取过程中使用了积分直方图以提高运算速度。在跟踪阶段，首先使用离线训练方式得到了目标类的子流形空间特征，然后使用子流形空间中特征与训练样本均值的距离作为相似性度量，采用粒子滤波框架进行跟踪。针对目标亮度、尺度、姿态变化以及存在遮挡等复杂条件下的视频跟踪结果验证了所提出方法的有效性和鲁棒性。

英文摘要：

In order to track objects steadily when illumination or pose changes, a novel approach based on histogram of oriented gradients （HOG） and submanifold was proposed. Firstly, regions of objects were divided into sub-regions to obtain HOG features separately; then features of sub-regions were combined and mapped into submanifold space using locality preserving projection（LPP）. Integral histogram was used to accelerate feature extraction. In the process of tracking, firstly, features of samples in submanifold space were trained off line, then, particle filter was used to accomplish tracking, where similarity was measured in submanifold space by distances between features of particles and the mean of training samples. Experimental results show that the proposed approach is effective and robust when illumination, scale and pose of objects change and objects are occluded.