Focusing on the issue that the Kernelized Correlation Filter (KCF) can not respond to the target scale change, a KCF target tracking algorithm based on fast discriminative scale estimation was proposed. Firstly, the target position was estimated by KCF. Then, a fast discriminative scale filter was learned online by using a set of target samples with different scales. Finally, an accurate estimation of the target size was obtained by applying the learned scale filter at the target position. The experiments were conducted on Visual Tracker Benchmark video sequence sets, and comparison was performed with the KCF algorithm based on Discriminative Scale Space Tracking (DSST) and the traditional KCF algorithm. Experimental results show that the tracking accuracy of the proposed algorithm is 2.2% to 10.8% higher than that of two contrast algorithms when the target scale changes, and the average frame rate of the proposed algorithm is also 19.1% to 68.5% higher than that of KCF algorithm based on DSST. The proposed algorithm has strong adaptability and high real-time performance to target scale change.

In order to solve the problem that deep learning ignores the local structure features of faces when it extracts face feature in face recognition, a novel face recognition approach which combines block Local Binary Pattern (LBP) and deep learning was presented. At first, LBP features were extracted from different blocks of a face image, which were connected together to serve as the texture description for the whole face. Then, the LBP feature was input to a Deep Belif Network (DBN), which was trained level by level to obtain classification capability. At last, the trained DBN was used to recognize unseen face samples. On ORL, YALE and FERET face databases, the experimental results show that the proposed method has a better recognition performance compared with Support Vector Machine (SVM) in small sample face recognition.

The algorithm of extracting pedestrian features based on texture information has the problems of redundant feature information and being unable to depict the human visual sensitivity, an algorithm named SF-LBP was proposed to extract pedestrian texture feature by Significant Local Binary Pattern which fuses the characteristics of human visual pedestrian system. Firstly, the algorithm calculated the significant factor in each region by saliency detection method. Then, it rebuilt the eigenvector of the image by significant factor weight and pedestrian texture feature, and generated the feature histogram according to local feature. Finally it integrated adaptive AdaBoost classifier to construct pedestrian detection system. The experimental results on INRIA database show that the SF-LBP feature achieves a detection rate of 97% and about 2%-3% higher than HOG (Histogram of Oriented Gradients) feature and Haar feature. It reaches recall rate of 90% and 2% higher than other features. It indicates that the SF-LBP feature can effectively describe the texture characteristics of pedestrians, and improve the accuracy of the pedestrian detection system.