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In actual traffic scenarios, the environment is complex and constantly changing, with many vehicles that have substantial similarities, posing significant challenges to vehicle tracking research based on deep learning. To address these challenges, this article investigates the application of the DeepSORT (simple online and realtime tracking with a deep association metric) multitarget tracking algorithm in vehicle tracking. Due to the strong dependence of the DeepSORT algorithm on target detection, a YOLOv5s_DSC vehicle detection algorithm based on the YOLOv5s algorithm is proposed, which provides accurate and fast vehicle detection data to the DeepSORT algorithm. Compared to YOLOv5s, YOLOv5s_DSC has no more than a 1% difference in optimal mAP0.5 (mean average precision), precision rate, and recall rate, while reducing the number of parameters by 23.5%, the amount of computation by 32.3%, the size of the weight file by 20%, and increasing the average processing speed of each image by 18.8%. After integrating the DeepSORT algorithm, the processing speed of YOLOv5s_DSC + DeepSORT reaches up to 25 FPS, and the system exhibits better robustness to occlusion. [ABSTRACT FROM AUTHOR]

In actual traffic scenarios, the environment is complex and constantly changing, with many vehicles that have substantial similarities, posing significant challenges to vehicle tracking research based on deep learning. To address these challenges, this article investigates the application of the DeepSORT (simple online and realtime tracking with a deep association metric) multitarget tracking algorithm in vehicle tracking. Due to the strong dependence of the DeepSORT algorithm on target detection, a YOLOv5s_DSC vehicle detection algorithm based on the YOLOv5s algorithm is proposed, which provides accurate and fast vehicle detection data to the DeepSORT algorithm. Compared to YOLOv5s, YOLOv5s_DSC has no more than a 1% difference in optimal mAP0.5 (mean average precision), precision rate, and recall rate, while reducing the number of parameters by 23.5%, the amount of computation by 32.3%, the size of the weight file by 20%, and increasing the average processing speed of each image by 18.8%. After integrating the DeepSORT algorithm, the processing speed of YOLOv5s_DSC + DeepSORT reaches up to 25 FPS, and the system exhibits better robustness to occlusion. [ABSTRACT FROM AUTHOR]

An online social network is a platform where people can communicate with friends, share information, speed up business development, and improve teamwork. A large amount of user privacy information existing in real social networks is leaked from person to person, and this issue has hardly been studied. With the rapid expansion of the network, the issue of privacy protection has received increasing attention. So far, many privacy protection methods including differential protection algorithms, encryption algorithms, access control strategies, and anonymization have been researched and applied. Information leakage means that the information shared by the user is disseminated or downloaded by his friends without the user's consent, and the transmission of private information will not be recorded. In order to track and find out the ways and methods of information leakage, this article adopts an unusual method, namely, the probability judgment based on trust. By screening the similarities between users, past information exchanges, and the topology of social networks, a trust model is established to evaluate and estimate the degree of trust between users. According to the rating information privacy of friends' trust, an information dissemination system is established, which can be applied to online social networking platforms to reduce the risk of information leakage, thereby ensuring the security of users' private information. At the same time, this paper expands the transmission system model without user authorization and proposes a fingerprint-based deterministic leak tracking algorithm. [ABSTRACT FROM AUTHOR]

Pedestrian detection and tracking based on computer vision has gradually become an international pattern recognition, which is one of the most active research topics in the field of computer vision and artificial intelligence. Using the theoretical results in the field of pattern recognition and computer vision technology, we are committed to detect and track pedestrians from video sequences. In addition to computer vision-based passer-by detection and tracking technology as the key, in the advanced computer vision action and analysis, it has a direct impact on the accuracy and robustness of its understanding. We analyzed various targets, such as subsequent recognition motion and pedestrian motion, and described them as high-level application processing, such as action understanding. In addition, because of the unique texture of human clothes compared with the surrounding natural landscape, they are highly "prominent" from the perspective of human visual system, and they are particularly prominent in the peripheral part of human contact with the background. In this paper, a binary function based on importance is proposed. As the space representation of image itself is not sensitive to noise and local signal, space representation is used. In addition, as an observation model, it can reduce the adverse effects of background noise and local noise on the tracking algorithm. Through the function block tracking, the pedestrian's body can be tracked in detail. At the same time, the color band learning method is used to update the target template online to deal with the changes of target appearance caused by sunshine, pedestrian posture, and other factors. According to the experimental results, even if the appearance and posture of pedestrians change greatly, it has a stable tracking effect. [ABSTRACT FROM AUTHOR]

In order to solve the problem of distrust of a large number of nodes under the overly centralized and centralized information transfer mode, a research method of real-time tracking algorithm of e-commerce logistics information based on blockchain technology is proposed. The application background of blockchain technology in real-time tracking algorithm of e-commerce logistics information is firstly described. Also, the blockchain technology, logistics information real-time tracking algorithm, cryptography technology, blockchain consensus algorithm, blockchain application scenarios, and other related technologies are analyzed in detail. Then, the blockchain model of real-time tracking of e-commerce logistics information is established, and it is found that data will be stored on the chain only when more than 51% of the nodes in the distributed system confirm that the data are valid. Otherwise, the data on the chain cannot be affected. Finally, the model description and hypothesis of real-time tracking of e-commerce logistics information based on blockchain technology are verified to provide guarantee for improving real-time tracking of e-commerce logistics information. [ABSTRACT FROM AUTHOR]

The purpose of this study was to investigate the state estimation problem for the multisensor descriptor fractional systems. Firstly, the descriptor fractional order system was transformed into two nondescriptor fractional order subsystem based on the singular value decomposition method; then, the descriptor fractional Kalman filters for the subsystems were proposed based on projection theory, which effectively solved the state estimation problem of the descriptor fractional order system with singular matrix; on this basis, the track fusion fractional Kalman filter of the multisensor descriptor fractional system is proposed by using the track fusion algorithm. The state estimation accuracy of multisensor descriptor fractional order systems is greatly improved. Simulation results show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Machine learning methods use computers to imitate human learning activities to discover new knowledge and enhance learning effects through continuous improvement. The main process is to further classify or predict unknown data by learning from existing experience and creating a learning machine. In order to improve the real-time performance and accuracy of the distributed EM algorithm for machine online learning, a clustering analysis algorithm based on distance measurement is proposed in combination with related theories. Among them, the greedy EM algorithm is a practical and important algorithm. However, the existing methods cannot simultaneously load a large amount of social information into the memory at a time. Therefore, we created a Hadoop cluster to cluster the Gaussian mixture model and check the accuracy of the algorithm, then compare the running time of the distributed EM algorithm and the greedy algorithm to verify the efficiency of the algorithm, and finally check the scalability of the algorithm by increasing the number of nodes. Based on this fact, this article has conducted research and discussion on the visualization of sports movements, and the teaching of visualization of sports movements can stimulate students' interest in physical education. The traditional physical education curriculum is completely based on the teacher's oral explanation and personal demonstration, and the emergence of visualized teaching of motor movements broke the teacher-centered teaching model and made teaching methods more interesting. This stimulated students' interest in sports and improved classroom efficiency. [ABSTRACT FROM AUTHOR]

Freestyle skiing U-shaped field is a snow sport that uses double boards to perform a series of action skills in a U-shaped pool, which requires very high skills for athletes. In this era of deep learning, in order to develop a more scientific training method, this paper combines multitarget tracking algorithm and deep learning to conduct research in freestyle skiing U-shaped venue skills motion capture. Therefore, this paper combines the convolutional neural network and multitarget tracking algorithm in deep learning to study the human action recognition technology, and then uses the LSTM module to study the freestyle skiing U-shaped venue skills. Finally, this paper designs the training method of the action recognition algorithm of the freestyle U-shaped skiing skills multitarget tracking algorithm based on deep learning. This paper also designs multitarget tracking dataset experiments and model updating experiments. Based on the data of experimental analysis, the training method designed in this paper is optimized, and finally compared with the traditional training method. Compared with the traditional freestyle U-shaped skiing skills training method, the experimental results show that the training method of the freestyle U-shaped skiing skills multitarget tracking algorithm action recognition algorithm is based on deep learning designed in this paper and this improves the skill score by 14.48%. Most professional students are very satisfied with the training method designed in this paper. [ABSTRACT FROM AUTHOR]

The vast majority of currently available kernelized correlation filter (KCF)-based trackers simply make use of a single object feature to define the object of interest. It is impossible to avoid tracking instability while working with a wide variety of complex videos. In this piece of research, an ensemble learning-based multi-cues fusion object tracking method is offered as a potential solution to the issue at hand. Using ensemble learning to train multiple kernelized correlation filters with different features in order to obtain the optimal tracking parameters is the primary concept behind the improved KCF-based tracking algorithm. After that, the peak side lobe ratio and the response consistency of two adjacent frames are used to obtain the fusion weight. In addition, an adaptive weighted fusion technique is applied in order to combine the response findings in order to finish the location estimation; finally, the tracking confidence is applied in order to update the tracking model in order to prevent model deterioration. In order to increase the adaptability of the revised algorithm to size-change, a Bayesian estimate model based on scale pyramid has been presented. This model is able to determine the optimal scale of the object, which is the goal of this endeavor. The tracking results of a number of different benchmark movies demonstrate that the algorithm that we have suggested is able to effectively eliminate the effects of interference elements, and that its overall performance is superior to that of the comparison algorithms. [ABSTRACT FROM AUTHOR]

Machine learning only uses single-channel grayscale features to model the target, and the filter solution process is relatively simple. When the target has a large change relative to the initial frame, the tracking effect is poor. When there is the same kind of target interference in the target search area, the tracking results will be poor. The tracking algorithm based on the fully convolutional Siamese network can solve these problems. By learning the similarity measurement function, the similarity between the template and the target search area is evaluated, and the target area is found according to the similarity. It adopts offline pre-training and does not update online for tracking, which has a faster tracking speed. According to this study, (1) considering the accuracy and speed, the target tracking algorithm based on correlation filtering performs well. A sample adaptive update model is introduced to eliminate unreliable samples, which effectively enhances the reliability of training samples. With simultaneous changes in illumination and scale, fast motion and in-plane rotation IPR can still be maintained. (2) Determined by calculating the Hessian matrix, in the Struck function, Bike3 parameter adjustment can achieve fast tracking, and Boat5 ensures that the system stability is maintained in the presence of interference factors. The position of the highest scoring point in the fine similarity score map of the same size as the search image is obtained by bicubic interpolation as the target position. (3) The parallax discontinuity caused by the object boundary cannot be directly processed as a smooth continuous parallax. The MeanShift vector obtained by calculating the target template feature and the feature to be searched can increase the accuracy by 53.1%, reduce the robustness by 31.8%, and reduce the error by 28.6% in the SiamVGG algorithm. [ABSTRACT FROM AUTHOR]

Because the scene of football video is fixed and simple, the events in football video, such as shooting and offside , also have clear semantics. At the same time, they have sufficient domain knowledge and have broad application prospects. The research framework of sports video intelligence analysis is usually regarded as a three-level framework, namely, the low-level feature layer, the middle-level key primitive generation layer, and the high-level event analysis layer. A MF_O2T (Moving Feature Online Target Tracking) algorithm is proposed. First, based on the marked first frame image, this algorithm extracts a set of standardized local images from the target areas of visible and infrared images as target convolution filters, removes the main color of the stadium by using HSV color space nonuniform quantization algorithm, and extracts the histogram of the main color of players in upper and lower blocks. Experimental results show that the algorithm designed in this paper has strong robustness, can better adapt to player tracking in different scenes of football video, and meets the real-time requirements of football training. [ABSTRACT FROM AUTHOR]

In order to improve the effectiveness of tennis teaching and enhance students' understanding and mastery of tennis standard movements, based on the three-dimensional (3D) convolutional neural network architecture, the problem of action recognition is deeply studied. Firstly, through OpenPose, the recognition process of human poses in tennis sports videos is discussed. Athlete tracking algorithms are designed to target players. According to the target tracking data, combined with the movement characteristics of tennis, real-time semantic analysis is used to discriminate the movement types of human key point displacement in tennis. Secondly, through 2D pose estimation of tennis players, the analysis of tennis movement types is achieved. Finally, in the tennis player action recognition, a lightweight multiscale convolutional model is proposed for tennis player action recognition. Meanwhile, a key frame segment network (KFSN) for local information fusion based on keyframes is proposed. The network improves the efficiency of the whole action video learning. Through simulation experiments on the public dataset UCF101, the proposed 3DCNN-based KFSN achieves a recognition rate of 94.8%. The average time per iteration is only 1/3 of the C3D network, and the convergence speed of the model is significantly faster. The 3DCNN-based recognition method of information fusion action discussed can effectively improve the recognition effect of tennis actions and improve students' learning and understanding of actions in the teaching process. [ABSTRACT FROM AUTHOR]

Computer vision is an interesting branch of artificial intelligence which is dedicated to how electronic devices can achieve the level of capabilities to perceive things just like ordinary human beings do. In order to solve the poor effect of video for the detection of target in football matches and the low accuracy of target tracking, this paper aims to make a deep exploration of the methods of video for the detection of target and tracking in football matches. The video moving for the detection of target method based on background model is used to extract the image in the background of the matching video which improves the light flow field. Secondly, the video differential image is acquired according to the difference of colors, the ghost target of the image in the video background model is scientifically determined, the ghost degree of the pixel points of the image is scientifically determined, and the flicker matrix of the target image is constructed. The number of pixels of the moving target is derived. A meanshift-based video target tracking algorithm is used in conjunction for the detection of target result to determine whether to track the target image until the overall video target tracking task is completed, move the central position of the target frame and background frame to the target position, select the best one to adapt to the target change, and determine whether to track the target image until the overall video target tracking task is completed. The simulation results suggest that the approach described in this study is capable of detecting and tracking moving objects, as well as improving target recognition and tracking accuracy. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence, the application of intelligent algorithms to low-power embedded chips has become a new research topic today. Based on this, this study optimizes the YOLOv2 algorithm by tailoring and successfully deploys it on the K210 chip to train the face object detection algorithm model separately. The intelligent fan with YOLOv2 model deployed in K210 chip can detect the target of the character and obtain the position and size of the character in the machine coordinates. Based on the obtained information of character coordinate position and size, the fan's turning Angle and the size of air supply are intelligently perceived. The experimental results show that the intelligent fan design method proposed here is a new embedded chip intelligent method of cutting and improving the YOLOv2 algorithm. It innovatively designed solo tracking, crowd tracking, and intelligent ranging algorithms, which perform well in human perception of solo tracking and crowd tracking and automatic air volume adjustment, improve the accuracy of air delivery and user comfort, and also provide good theoretical and practical support for the combination of AI and embedded in other fields. [ABSTRACT FROM AUTHOR]

With the development of computer hardware technology, the real-time problem of visual target tracking algorithm increasingly depends on hardware solutions. The core problem of visual target tracking is how to enhance the robustness of tracking algorithm to various complex background environments and various interference factors. Aiming at overcoming the defect that the traditional SLAM (simultaneous localization and map building) algorithm based on EKF (extended Kalman filter) has a slow repair speed for environmental interference, a Monocular SLAM_WOCPF (Monocular vision SLAM based on weight optimization combined particle filter) algorithm is proposed. The weights of all particles are reoptimized in the particle set and they are combined with the tendency of particles to degenerate and deplete. In this way, the chance of self replication of low weight particles is increased, thus increasing the diversity of the whole sample. Furthermore, the improved PF (particle filter) algorithm is applied to solve the problem of road sign observation of mobile robots, so as to expand its application scope. The results show that the mean road sign errors of the Monocular SLAM_WOCPF algorithm in two noise environments are 0.332/m and 0.441/m. The conclusion shows that the Monocular SLAM_WOCPF road sign observation method proposed in this paper can effectively improve the matching success rate of visual road signs and improve the observation quality. [ABSTRACT FROM AUTHOR]

In order to improve the effect of e-sports training, this paper combines the intelligent gesture recognition technology to construct an e-sports training system and judges the training effect of players through the recognition of players' gestures. Moreover, this paper studies the commonly used feature extraction algorithms and proposes an improved SLC-Harris feature extraction algorithm, and the feasibility of this algorithm is verified by the experimental results on the EUROC data set. In addition, this paper uses the KLT optical flow algorithm to track the extracted feature points and calculates the pure visual pose through epipolar geometry, triangulation, and PnP algorithms. The experimental research results show that the electronic economic training system based on intelligent gesture recognition proposed in this paper has certain effects. [ABSTRACT FROM AUTHOR]

With the development of computer hardware technology, the real-time problem of visual target tracking algorithm increasingly depends on hardware solutions. The core problem of visual target tracking is how to enhance the robustness of tracking algorithm to various complex background environments and various interference factors. Aiming at overcoming the defect that the traditional SLAM (simultaneous localization and map building) algorithm based on EKF (extended Kalman filter) has a slow repair speed for environmental interference, a Monocular SLAM_WOCPF (Monocular vision SLAM based on weight optimization combined particle filter) algorithm is proposed. The weights of all particles are reoptimized in the particle set and they are combined with the tendency of particles to degenerate and deplete. In this way, the chance of self replication of low weight particles is increased, thus increasing the diversity of the whole sample. Furthermore, the improved PF (particle filter) algorithm is applied to solve the problem of road sign observation of mobile robots, so as to expand its application scope. The results show that the mean road sign errors of the Monocular SLAM_WOCPF algorithm in two noise environments are 0.332/m and 0.441/m. The conclusion shows that the Monocular SLAM_WOCPF road sign observation method proposed in this paper can effectively improve the matching success rate of visual road signs and improve the observation quality. [ABSTRACT FROM AUTHOR]

Because of the overwhelming characteristics of computer vision technology, the trend of intelligent upgrading in sports industry is obvious. Video technical and tactical data extraction, big data analysis, and match assistance systems have caused profound changes to all aspects of the sports industry. One of the important applications is the playback and analysis of sports videos. People can observe the videos and summarize the experience of sports matches, and in this process, people prefer the computers to also interpret and analyze sports matches, which can not only help coaches in postmatch analysis but also design robots to assist in teaching and training. In this paper, we have examined and designed an automatic detection system for ping pong balls, in which the motion trajectory and rotation information of ping pong balls are mainly detected. To achieve this goal, the detection and tracking algorithm of ping pong balls based on deep neural network is used, and better results are achieved on the data set established by ourselves and the actual system test. After obtaining the position of the ping pong ball in the image, the rotation direction and speed of the ping pong ball are calculated next, and the Fourier transform-based speed measurement method and the CNN-based rotation direction detection method are implemented, which achieve better results in the testing of lower speed datasets. Finally, this paper proposes an LSTM-based trajectory prediction algorithm to lay the foundation for the design of table tennis robot by predicting the trajectory of table tennis. Experimental tests show that the proposed system can better handle the ping pong ball tracking and rotation measurement problems. [ABSTRACT FROM AUTHOR]

Target tracking is an important subject in computer vision technology, which has developed rapidly in recent ten years, and its application have become wider and wider. In this process, it has transferred from a simple experimental tracking environment to a complex real scene where more challenges need to be solved. The rapid development of deep learning has promoted the research progress of digital vision. Target tracking technology is an important foundation of digital vision research, which makes it develop from academia to industry. In this paper, a method of target tracking using MDNet is introduced. Starting with the attention mechanism, two attention mechanisms are added to extract and integrate the better features. Case partitioning is used to reduce the investment of tracking module and minimize the network size during tracking, and its result can be prevented from getting worse. Finally, the experiment is analyzed in detail. [ABSTRACT FROM AUTHOR]

Eye tracking is currently a research hotspot in the territory of service robotics. There is an urgent need for machine vision technique in the territory of video surveillance, and biological visual object following is one of the important basic research problems. By tracking the object of interest and recording the tracking trajectory, we can extract a structure from a video. It can also analyze the abnormal behavior of groups or individuals in the video or assist the public security organs in inquiring and searching for evidence of criminal suspects, etc. Moving object following has always been one of the frontier topics in the territory of machine vision, and it has very important appliances in mobile robot positioning and navigation, multirobot formation, lunar exploration, and intelligent monitoring. Moving object following has always been one of the frontier topics in the territory of machine vision, and it has very important appliances in mobile robot positioning and navigation, multirobot formation, lunar exploration, and intelligent monitoring. Moving object following in visual surveillance is easily affected by factors such as occlusion, rapid object movement, and appearance changes, and it is difficult to solve these problems effectively with single-layer features. This paper adopts a visual object following algorithm based on visual information features and few-shot learning, which effectively improves the accuracy and robustness of tracking. [ABSTRACT FROM AUTHOR]

Maintaining the safe and efficient operation of network technology is an important development task of the computer industry. Topology constraint can optimize and combine the tracking results and select the target objects with better tracking performance to obtain the final tracking results and determine the target scale changes. Data mining technology can reduce the number of combinations to be detected, reduce the workload, and improve the timeliness and accuracy of the process of mining alarm association rules. Therefore, based on the summary and analysis of previous research results, this paper studied the network fault diagnosis of the embedded system method based on topology constraint and data mining. Firstly, a fault diagnosis topology model was established by constructing a topology search algorithm, which eliminated the filtering of association rules without topology relationship; the association rule-based data mining model was analyzed through the collection of network alarm data; the model algorithm was applied to the simulation experiment of network fault diagnosis of the embedded system and achieved good results. The results show that correcting rage of retrieval varies from 0.65 to 090 under different window sizes; the running time of the proposed method drops from 310 s to 35 s during 1–8 step/s of the sliding step, while the node degree ranges from 8 to 14 and diagnostic accuracy ranges from 0.97 to 0.94; the remaining alarm number increases from 0.5 to 3.5 threshold value, while the regular association number distributed in an interval of 40 to 140. The algorithm in this paper provides a reference for further research on network fault diagnosis of the embedded system. [ABSTRACT FROM AUTHOR]

Based on video, the human movement can be analyzed to achieve scientific training and skill improvement. Specifically, according to the video data during the movement, the human body can be detected and tracked, and relevant trajectory data can be obtained. On this basis, key motion parameters can be obtained and quantitative analysis of motion can be achieved. This paper uses video processing technologies to analyze the long jump posture in physical education. According to the video sequences measured during the athlete's long jump, the target detection and tracking algorithms are used to obtain the athlete's trajectory after preprocessing. Afterwards, further processing is carried out to calculate speed, angle, posture, and other related information to assist scientific sports training. The experimental results based on the measured data show that the algorithm can realize the analysis of the long jump scene and complete the quantitative analysis of the key indicators of the athletes. The research results can effectively support school physical education and guidance training and also provide a reference for other competitive video analysis. [ABSTRACT FROM AUTHOR]

With the rapid development of deep learning algorithms, it is gradually applied in UAV (Unmanned Aerial Vehicle) driving, visual recognition, target tracking, behavior recognition, and other fields. In the field of sports, many scientists put forward the research of target tracking and recognition technology based on deep learning algorithms for athletes' trajectory and behavior capture. Based on the target tracking algorithm, a regional proposal network RPN algorithm combined with the twin regional proposal network Siamese algorithm is proposed to study the tracking and recognition technology of athletes' behavior. Then, the adaptive updating network is used to track the behavior target of athletes, and the simulation model of behavior recognition is established. This algorithm is different from the traditional twin network algorithm. It can accurately take the athlete's behavior as the target candidate box in model training and reduce the interference of environment and other factors on model recognition. The results show that the Siamese-RPN algorithm can reduce the interference from the background and environment when tracking the athletes' target behavior trajectory. This algorithm can improve the training behavior recognition model, ignore the background interference elements of the behavior image, and improve the accuracy and overall performance of the model. Compared with the traditional twin network method for sports behavior recognition, the Siamese-RPN algorithm studied in this paper can perform offline operations and distinguish the interference factors of athletes' background environment. It can quickly capture the characteristic points of athletes' behavior as the data input of the tracking model, so it has excellent popularization and application value. [ABSTRACT FROM AUTHOR]

With the advent of the artificial intelligence era, target adaptive tracking technology has been rapidly developed in the fields of human-computer interaction, intelligent monitoring, and autonomous driving. Aiming at the problem of low tracking accuracy and poor robustness of the current Generic Object Tracking Using Regression Network (GOTURN) tracking algorithm, this paper takes the most popular convolutional neural network in the current target-tracking field as the basic network structure and proposes an improved GOTURN target-tracking algorithm based on residual attention mechanism and fusion of spatiotemporal context information for data fusion. The algorithm transmits the target template, prediction area, and search area to the network at the same time to extract the general feature map and predicts the location of the tracking target in the current frame through the fully connected layer. At the same time, the residual attention mechanism network is added to the target template network structure to enhance the feature expression ability of the network and improve the overall performance of the algorithm. A large number of experiments conducted on the current mainstream target-tracking test data set show that the tracking algorithm we proposed has significantly improved the overall performance of the original tracking algorithm. [ABSTRACT FROM AUTHOR]

Building the first Russian-Vietnamese neural machine translation system, we faced the problem of choosing a translation unit system on which source and target embeddings are based. Available homogeneous translation unit systems with the same translation unit on the source and target sides do not perfectly suit the investigated language pair. To solve the problem, in this paper, we propose a novel heterogeneous translation unit system, considering linguistic characteristics of the synthetic Russian language and the analytic Vietnamese language. Specifically, we decrease the embedding level on the source side by splitting token into subtokens and increase the embedding level on the target side by merging neighboring tokens into supertoken. The experiment results show that the proposed heterogeneous system improves over the existing best homogeneous Russian-Vietnamese translation system by 1.17 BLEU. Our approach could be applied to building translation bots for language pairs with different linguistic characteristics. [ABSTRACT FROM AUTHOR]

In the optimization of problems in dynamic environments, algorithms need to not only find the global optimal solutions in a specific environment but also to continuously track the moving optimal solutions over dynamic environments. To address this requirement, a species conservation-based particle swarm optimization (PSO), combined with a spatial neighbourhood best searching technique, is proposed. This algorithm employs a species conservation technique to save the found optima distributed in the search space, and these saved optima either transferred into the new population or replaced by the better individual within a certain distance in the subsequent evolution. The particles in the population are attracted by its history best and the optimal solution nearby based on the Euclidean distance other than the index-based. An experimental study is conducted based on the moving peaks benchmark to verify the performance of the proposed algorithm in comparison with several state-of-the-art algorithms widely used in dynamic optimization problems. The experimental results show the effectiveness and efficiency of the proposed algorithm for tracking the moving optima in dynamic environments. [ABSTRACT FROM AUTHOR]

Pedestrian tracking is a critical problem in the field of computer vision. Particle filters have been proven to be very useful in pedestrian tracking for nonlinear and non-Gaussian estimation problems. However, pedestrian tracking in complex environment is still facing many problems due to changes of pedestrian postures and scale, moving background, mutual occlusion, and presence of pedestrian. To surmount these difficulties, this paper presents tracking algorithm of multiple pedestrians based on particle filters in video sequences. The algorithm acquires confidence value of the object and the background through extracting a priori knowledge thus to achieve multipedestrian detection; it adopts color and texture features into particle filter to get better observation results and then automatically adjusts weight value of each feature according to current tracking environment. During the process of tracking, the algorithm processes severe occlusion condition to prevent drift and loss phenomena caused by object occlusion and associates detection results with particle state to propose discriminated method for object disappearance and emergence thus to achieve robust tracking of multiple pedestrians. Experimental verification and analysis in video sequences demonstrate that proposed algorithm improves the tracking performance and has better tracking results. [ABSTRACT FROM AUTHOR]