Search

In recent years, due to the growing popularity of the human network behavior, we meet the new generation of big data era. This makes the emergence of characteristics that large data storage capacity and fast business growth. For large storage capacity, instability, low retrieval time caused by large data information, this paper demonstrate a distributed storage and server clustering approach to solve the problem of tradition storage capacity, high-availability of retrieval and servers. For the arrival of the era of big data, how to improve the retrieval time, the storage capacity and stability of system, these provide for the future construction of a guiding role.

Recent years, many vision-based vehicle detection methods have been proposed. But just a few of them paid attention to vehicle type classification in a real-time video, which is an important part in intelligent traffic system. The Haar features and Histograms of Oriented Gradients (HOG) features have been proposed as useful features for vehicle detection respectively. In this paper, we propose a method based on combined the Haar features and HOG features to detect vehicles in videos and classify them into two types. The experiment results show that this method can classify and detect the vehicles in multi-orientations with good classification results, and it verified the effectiveness of the propose method with real vehicle samples.

An approach of 3D face recognition by using of facial surface classification image and PCA is presented. In the step of pre-processing, the scattered 3D points of a facial surface are normalized by surface fitting algorithm using multilevel B-splines approximation. Then, partial-ICP method is utilized to adjust 3D face model to be in the right front pose for a better recognition performance. By using the normalized facial depth image been acquired through the two previous steps, and by calculating the Gaussian and mean curvatures at each point, the surface types are classified and the classification result is used to mark different kinds of area on the facial depth image by 8 gray-levels. This achieved gray image is named as Surface Classification Image (SCI) and the SCI now represents the 3D features of the face and then it is input to the process of PCA to obtain the SCI eigenfaces to recognize the face. In the experiments conducted on 3D Facial database ZJU-3DFED of Zhejiang University, we obtained the rank-1 identification score of 94.5%, which outperformed the result of using PCA method directly on the face depth image (instead of SCI) by 16.5%.