The current public datasets of Pedestrian Attribute Recognition (PAR) have the characteristics of complicated attribute annotations and various collection scenarios, leading to the large variations of the pedestrian attributes in different datasets, so that it is hard to directly utilize the existing labeled information in the public datasets for PAR in practice. To address this issue, a multi-source and multi-label PAR method based on domain adaptation was proposed. Firstly, to transfer the styles of the different datasets into a unified one, the features of the samples were aligned by the domain adaption method. Then, a multi-attribute one-hot coding and weighting algorithm was proposed to align the labels with the common attribute in multiple datasets. Finally, the multi-label semi-supervised loss function was combined to perform joint training across datasets to improve the attribute recognition accuracy. The proposed feature alignment and label alignment algorithms were able to effectively solve the heterogeneity problem of attributes in multiple PAR datasets. Experimental results after aligning three pedestrian attribute datasets PETA, RAPv1 and RAPv2 with PA-100K dataset show that the proposed method improves the average accuracy by 1.22 percentage points, 1.62 percentage points and 1.53 percentage points respectively, compared to the method StrongBaseline, demonstrating that this method has a strong advantage in cross dataset PAR.
The texture of power line inspection images with parallax variation is complex, the number of paired matching points obtained by traditional algorithms is less and the registration accuracy is low, which seriously affect the stitching effect of power line inspection unmanned aerial vehicle image. In order to solve the problems, a new image stitching method based on improved Order-Aware Network (OANet) was proposed. Firstly, the Accelerated KAZE (AKAZE) algorithm was adopted to match the power line inspection images to be stitched roughly. Secondly, the Squeeze-and-Excitation Networks (SENet) was added to the Order-Aware module in OANet, which helped to enhance the grasping ability of the network for both the local and global context information, and more accurate paired matching points were obtained. Then, the Mesh-based Photometric Alignment (MPA) algorithm was used to register the images to be stitched. Finally, the optimal suture line in the overlapping area was calculated by the content compressed sensing algorithm to complete image stitching. The number of correct matching points of the improved OANet network is about 10% higher than that of the original OANet network with time consumption increased by 10 ms on average. Compared with the registration stitching algorithms such as As-Projective-As-Possible (APAP) algorithm, Adaptive As-Natural-As-Possible (AANAP) algorithm and MPA algorithm, the proposed algorithm has the highest stitching quality with the root mean square error of the overlapping area of the images to be stitched is 0 and no distortion in the non-overlapping area. Experimental results show that, the proposed algorithm can stitch the aerial images of power line inspection quickly and stably.
The virtual machines in cloud computing platform exchange data in the shared memory of physical machine. In view of the problem that the traffic cannot be captured and detected in firewall or other security components, the OpenFlow technology was analyzed, and a traffic redirection method based on OpenFlow was presented. To control traffic forwarding process and redirect it to security components, the method provided network connection for virtual machines with OpenFlow controller and virtual switches instead of physical switches, and built a traffic detection system composed of four modules including virtual switch, control unit, intrusion detection and system configuration management. The experimental results show that the proposed scheme can realize traffic redirection and the subsequent detection processing, and the system can provide switch-level and host-level control granularity. It also solves traffic detection problem under cloud computing environment in traditional scene by traffic redirection, and provides great expansion of the traffic processing based on OpenFlow.
For the traditional player skill estimation algorithms based on probabilistic graphical model neglect the first-move advantage (or home play advantage) which affects estimation accuracy, a new method to model the first-move advantage was proposed. Based on the graphical model, the nodes of first-move advantage were introduced and added into player's skills. Then, according to the game results, true skills and first-move advantage of palyers were caculated by Bayesian learning method. Finally, predictions for the upcoming matches were made using those estimated results. Two real world datasets were used to compare the proposed method with the traditional model that neglect the first-move advantage. The result shows that the proposed method can improve average estimation accuracy noticeably.