An Easily Initialized Visual Tracking Algorithm Based on Similar Structure for Convolutional Neural Network
LI Huan-y · JOURNAL OF ELECTRONICS INFORMATION TECHNOLOGY · 2016
On the issues about the robustness in visual object tracking, based on Principal Component Analysis(PCA) and Convolutional Neural Network(CNN), a novel visual tracking algorithm with deep feature, which is acquired from a easily initialized CNN structure, is proposed. First, the original image is processed by affine transformation. Next, layered PCA learning is used to process the normalized size image, the eigenvectors learned by PCA are used to be the filters of a CNN structure to realize initialization. Then, the deep expression of the object is extracted by this CNN structure. Finally, combining particle filter algorithm, a simple logistic regression classifier is used to realize target tracking. The result shows that the deep feature acquired from the easily initialized CNN structure has a better expressivity, it can distinguish the object and background effectively. The proposed algorithm has a better inflexibility to illumination, occlusion, rotation and camera shake, and it exhibits a good robustness and accuracy in many video sequences.