To solve the problem of inaccurate localization of model which is caused by inaccurate manual labeling in 3D point cloud training data, a novel robust 3D object detection method based on localization uncertainty was proposed. Firstly, with the 3D voxel grid-based Sparsely Embedded CONvolutional Detection (SECOND) network as basic network, the prediction of localization uncertainty was added based on Region Proposal Network (RPN). Then, during the training process, the localization uncertainty was modeled by using Gaussian and Laplace distribution models, and the localization loss function was redefined. Finally, during the prediction process, the threshold filtering and Non-Maximum Suppression (NMS) methods were performed to filter candidate objects based on the object confidence which was consisted of the localization uncertainty and classification confidence. Experimental results on the KITTI 3D object detection dataset show that compared with SECOND network, the proposed algorithm has the detection accuracy improved by 0.5 percentage points on car category at moderate level. The detection accuracy of the proposed algorithm is 3.1 percentage points higher than that of SECOND network with adding disturbance simulation noise to the training data in the best case. The proposed algorithm improves the accuracy of 3D object detection, which reduces false detection and improves the accuracy of 3D bounding boxes, and is more robust to noisy data.

In binocular stereo vision, the camera needs to be calibrated to obtain its internal and external parameters in 3D measurement or precise positioning of the object.Through the study of the camera model with first-order radial distortion, linear formulas of solving internal and external parameters were constructed based on Radial Alignment Constraint (RAC) calibration method. Inclination angle, rotation angle, pitch angle and main distortion elements of lens were taken into consideration in this algorithm, which modified the defects in the traditional RAC calibration method that it only considers radial distortion and some parameters need priori values. The 3D reconstruction experiment of multi-pose binocular camera was carried out by using the obtained internal and external parameters. The experimental results show that,the reprojection error of this calibration method is distributed in[-0.3,0.3], and the similarity between the measurement trajectory and the actual trajectory is 96%, which has a positive effect on reducing the error rate of binocular stereo vision 3D measurement.

Focusing on the issue that the HBase storage without spatio-temporal index degrades the traffic data query performance, some HBase spatio-temporal indexes based on row keys were proposed for massive traffic data. Firstly, the dimensionality reduction method based on Geohash was used to convert two-dimensional spatial position data into a one-dimensional code. Then the code was combined with the temporal dimension. Secondly, four index models were put forward based on combination order, and the structures of the models and their adaption conditions for traffic data query were discussed. Finally, the algorithm of index creation as well as traffic data query algorithm was proposed. Experimental results show that the proposed HBase spatio-temporal index structure can effectively enhance the traffic data query performance. In addition, the query performance of four different spatio-temporal index structures in different data size, different query radius and different query time range were compared, which verified the different adaption scenes of different index structures in traffic data query.

In Video-On-Demand (VOD) applications, it is desired that the encrypted multimedia data are still partially perceptible after encryption in order to stimulate the purchase of the high-quality versions of the multimedia products. This perceptual encryption requires specific algorithms for encrypting the video data. Duo to lack of H.264 video perceptual encryption algorithms for mobile communication application, a video encryption algorithm based on ZU Chongzhi's (ZUC) algorithm and Compressive Sensing (CS) was proposed. First of all, ZUC algorithm was utilized to construct a random measurement matrix. Then the quantified Discrete Cosine Transformation (DCT) coefficients were measured by measurement matrix, and the measured values were regarded as new quantified DCT coefficients to encode, which realized the encryption by using the difference between original and new coefficients. Finally, the characteristics of a good perception encryption algorithm were defined. The experimental results show that the proposed algorithm has little effect on video compression bit rate with low time complexity, and it is also sensitive to key change with good perceptual security.