MLSS-VO: A Multi-Level Scale Stabilizer with Self-Supervised Features for Monocular Visual Odometry in Target Tracking

In this study, a multi-level scale stabilizer intended for visual odometry (MLSS-VO) combined with a self-supervised feature matching method is proposed to address the scale uncertainty and scale drift encountered in the field of monocular visual odometry. Firstly, the architecture of an instance-level recognition model is adopted to propose a feature matching model based on a Siamese neural network. Combined with the traditional approach to feature point extraction, the feature baselines on different levels are extracted, and then treated as a reference for estimating the motion scale of the camera. On this basis, the size of the target in the tracking task is taken as the top-level feature baseline, while the motion matrix parameters as obtained by the original visual odometry of the feature point method are used to solve the real motion scale of the current frame. The multi-level feature baselines are solved to update the motion scale while reducing the scale drift. Finally, the spatial target localization algorithm and the MLSS-VO are applied to propose a framework intended for the tracking of target on the mobile platform. According to the experimental results, the root mean square error (RMSE) of localization is less than 3.87 cm, and the RMSE of target tracking is less than 4.97 cm, which demonstrates that the MLSS-VO method based on the target tracking scene is effective in resolving scale uncertainty and restricting scale drift, so as to ensure the spatial positioning and tracking of the target.

Research on Fast Compensation Algorithm for Interframe Motion of Multimedia Video Based on Manhattan Distance

To improve the video quality, aiming at the problems of low peak signal-to-noise ratio, poor visual effect, and low bit rate of traditional methods, this paper proposes a fast compensation algorithm for the interframe motion of multimedia video based on Manhattan distance. The absolute median difference based on wavelet transform is used to estimate the multimedia video noise. According to the Gaussian noise variance estimation result, the active noise mixing forensics algorithm is used to preprocess the original video for noise mixing, and the fuzzy C-means clustering method is used to smoothly process the noisy multimedia video and obtain significant information from the multimedia video. The block-based motion idea is to divide each frame of the video sequence into nonoverlapping macroblocks, find the best position of the block corresponding to the current frame in the reference frame according to the specific search range and specific rules, and obtain the relative Manhattan distance between the current frame and the background of multimedia video using the Manhattan distance calculation formula. Then, the motion between the multimedia video frames is compensated. The experimental results show that the algorithm in this paper has a high peak signal-to-noise ratio and a high bit rate, which effectively improves the visual effect of the video.

Influence of the Weld Joint Position on the Mechanical Stress Concentration in the Construction of the Alternative Skid Car System’s Skid Chassis

This paper deals with the optimization of the crossbars, parts of the existing frame of the experimental system of the Alternative SkidCar. This part plays a crucial role and is designed to enable and ensure reduced adhesion conditions between the vehicle and the road. To this end, its optimization targeted here is performed using both analytical calculations and simulations in MSC Adams software, wherein the loading forces and boundary conditions on the frame support wheels are obtained considering the static conditions, as well as the change of the direction of travel. The least favourable load observed was used, later on, as the input value for the strength analysis of the frame. The analysis was performed using the finite element method (FEM) in SolidWorks. Based on the linear and nonlinear analyses performed, the course of stress on the frame arms and critical points with the highest stress concentration were determined. Subsequently, according to the results obtained, a new design for the current frame was proposed and, thereby, warrants greater rigidity, stability and strength to the entire structure, while reducing its weight and maximizing the potential of the selected material. The benefit of the current contribution lies in the optimization of the current frame shape, in terms of the position of weld joints, the location of the reinforcements and the thickness of the material used.

Advances in identifying GM plants: current frame of the detection of transgenic GMOs

Transgenic GMOs were welcomed in the 1990s due to the difficulties distinguishing genetic and epigenetic modifications from random mutagenesis and their ability to insert new nucleic sequences more rapidly but still randomly. Their marketing in Europe has been accompanied by health and environmental risk assessments, specific monitoring and traceability procedures to preserve the free choice of consumers and allow the coexistence of different supply chains. This chapter reviews the regulations, detection techniques, strategies and standards that have been put in place in the European Union since 1996 to ensure the analytical traceability of these GMOs. The capacity of the matrix approach, initially targeted at transgenic GMOs, to trace other types of GMOs is discussed in an accompanying chapter.

Real-time Vehicle Surveillance System Based on Image Processing and Short Message Service

This paper proposes a real-time vehicle surveillance system based on image processing approach tailored with short message service. A background subtraction, color balancing, chain code based shape detection, and blob filtering are used to detect suspicious moving human around the parked vehicle. Once detected, the developed system will generate a warning notification to the owner by sending a short message to his mobile phone. The current frame of video image will also be stored and be sent to the owner e-mail for further checking and investigation. Last stored image will be displayed in a centralized monitoring website, where the status of the vehicle also can be monitored at the same time. When necessary, the stored images can be used during investigation process to assist the authority to take further legal actions.

Innovative Research on the Development of Online Education Mode of Internet Thinking Based on the Discrimination of Learning Attention under the Analysis of Head Posture

With the rapid development of Internet technology and the popularity of 5G and broadband, online education in China, especially mobile online education, is in full swing. Based on the development status of online education in China, this paper analyzes the innovative application of learning attention discrimination based on head posture analysis in the development of online education mode of Internet thinking. Learning attention is an important factor of students’ learning efficiency, which directly affects students’ learning effect. In order to effectively monitor students’ learning attention in online teaching, a method of distinguishing students’ learning attention based on head posture recognition is proposed. In the tracking process, as long as the head angle of the current frame is close to the head angle of the key frame in a certain scale model, the visual angle apparent model can reduce the error accumulation in large-scale tracking. A Dynamic Bayesian Network (DBN) model is used to reason students’ Learning Attention Goal (LAG), which combines the relationships among multiple LAGs, multiple students’ positions, multicamera face images, and so on. We measure the head posture through the similarity vector between the face image and multiple face categories without explicitly calculating the specific head posture value. The test results show that the proposed model can effectively detect students’ learning attention and has a good application prospect.

Research on the Generation and Design Method of Residential Community Scheme Based on Improving Design

Along with the urban renewal and development, the urban living environment has given rise to various problems that need to be solved. With an eye on the future development model of residential communities, an experimental preliminary design for the construction of architectural space, public space, and landscape space based on people’s actual needs is carried out in an attempt to alleviate the more urgent symbiotic relationship between people and urban environment. To this end, this paper proposes a planning and design generation framework for the constructed external spatial environment of building groups based on a recursive double-adversarial network model. Firstly, we extract the features of the constructed external spatial environment of the building group in depth and generate the expression feature map, which is used as a supervisory signal to generate an expression seed image of the constructed external spatial environment of the building group; then we use the generated seed image together with the constructed external spatial environment of the original target building group as the input to generate a feature-holding image as the output of the current frame, and the feature-holding image is also used as the input for the next. Finally, the seed image generation network and the feature-holding image generation network are recursively used to generate the next frame, and the video sequence of the expressions of the constructed external spatial environment of the building group with the same feature-holding expressions as the original input is recursively obtained several times. The experimental results on the building group database show that the proposed method can generate clear and natural video frames of the constructed external spatial environment of the building group, which can be gradually derived from the design of building units to the construction of the building group and penetrate into the planning and design of the external spatial environment in order to comprehensively improve the living environment of urban population and provide a design method and theoretical support for the design of future urban residential communities.

Lane and Road Marker Semantic Video Segmentation Using Mask Cropping and Optical Flow Estimation

Lane and road marker segmentation is crucial in autonomous driving, and many related methods have been proposed in this field. However, most of them are based on single-frame prediction, which causes unstable results between frames. Some semantic multi-frame segmentation methods produce error accumulation and are not fast enough. Therefore, we propose a deep learning algorithm that takes into account the continuity information of adjacent image frames, including image sequence processing and an end-to-end trainable multi-input single-output network to jointly process the segmentation of lanes and road markers. In order to emphasize the location of the target with high probability in the adjacent frames and to refine the segmentation result of the current frame, we explicitly consider the time consistency between frames, expand the segmentation region of the previous frame, and use the optical flow of the adjacent frames to reverse the past prediction, then use it as an additional input of the network in training and reasoning, thereby improving the network’s attention to the target area of the past frame. We segmented lanes and road markers on the Baidu Apolloscape lanemark segmentation dataset and CULane dataset, and present benchmarks for different networks. The experimental results show that this method accelerates the segmentation speed of video lanes and road markers by 2.5 times, increases accuracy by 1.4%, and reduces temporal consistency by only 2.2% at most.

Children's feelings and attitude towards medical contacts

Introduction and purpose Medical procedures can be physically harmful and cause psychological trauma among young children, possibly resulting in certain lifelong aversion. The aim of the study was to evaluate children's feelings while undergoing minor medical procedures depending on age, gender and previous experience and to compare it with university students. Material and methods We conducted a survey among 382 primary school children, as well as 334 university students. Questions concerned the frame of mind at the moment of examination, during dentist and general physician appointment, vaccination, hospitalization and condition of sore throat. Results The experience of hospital stay resulted in better feelings about possible future hospitalization in both groups, and about dentist appointment in the group of children. Girls and women declared lower mood during general physician appointment. Interestingly, in the group of children boys felt worse at the moment of survey, while in the group of students women felt so. The current frame of mind correlated with an appraisal of all other situations among students, whereas only with physician appointment and vaccination among children. All of above were statistically significant. For both groups, the most unpleasant condition was sore throat. However, in regards to medical procedures exclusively, vaccination appeared to be the most traumatic for children and hospitalization for students. Conclusion Earlier experience and gender of children can affect their feelings about medical procedures. Presented analysis suggests that every medical should do their best to provide pleasant atmosphere for every child during medical contacts.

SFGAN: Unsupervised Generative Adversarial Learning of 3D Scene Flow from the 3D Scene Self

3D scene flow presents the 3D motion of each point in the 3D space, which forms the fundamental 3D motion perception for autonomous driving and server robots. Although the RGBD camera or LiDAR capture discrete 3D points in space, the objects and motions usually are continuous in the macro world. That is, the objects keep themselves consistent as they flow from the current frame to the next frame. Based on this insight, the Generative Adversarial Networks (GAN) is utilized to self-learn 3D scene flow with no need for ground truth. The fake point cloud of the second frame is synthesized from the predicted scene flow and the point cloud of the first frame. The adversarial training of the generator and discriminator is realized through synthesizing indistinguishable fake point cloud and discriminating the real point cloud and the synthesized fake point cloud. The experiments on KITTI scene flow dataset show that our method realizes promising results without ground truth. Just like a human observing a real-world scene, the proposed approach is capable of determining the consistency of the scene at different moments in spite of the exact flow value of each point is unknown in advance. Corresponding author(s) Email: [email protected]