Improved average consensus scalable algorithm of target tracking for wireless sensor network

Target detection and tracking is a key technology in intelligent monitoring system. However, it is still difficult to design a robust, accurate and real-time target detection and tracking algorithm. Taking target Tracking as the research focus, this paper focuses on one of the current mainstream algorithms, namely, TLD (tracking-learning-detection) target Tracking algorithm. Based on the theory of TLD target tracking and corner detection, a new target tracking method was proposed to solve the existing problems. A classifier is built online to track and learn the target in real time, and a target contour detector is added in the system to complement the random forest detector to solve the problem of tracking failure. Experimental results show that the proposed algorithm can effectively overcome the influence of fast change or depth occlusion, and improve the tracking efficiency of moving targets.

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> In this paper, we address the problem of target tracking in a collaborative acoustic sensor network. To cope with the inherent characteristics and constraints of wireless sensor networks, we present a novel target-tracking algorithm with power-aware concerns. The underlying tracking methodology is described as a multiple-sensor tracking/fusion technique based on particle filtering. As discussed in the most recent literature, particle filtering is defined as an emerging Monte Carlo state estimation technique with proven superior performance in many target-tracking applications. More specifically, in our proposed method, each activated sensor transmits the received acoustic intensity and the direction of arrival (DOA) of the target to the sensor fusion center (a dedicated computing and storage platform, such as a microserver). The fusion center uses each received DOA to generate a set of estimations based on the state partition technique, as described later in this paper. In addition, a set of sensor weights is calculated based on the acoustic intensity received by each activated sensor. Next, the weighted sum of the estimates is used to generate the proposal distribution in the particle filter for sensor fusion. This technique renders a more accurate proposal distribution and, hence, yields more precise and robust estimations of the target using fewer samples than those of the traditional bootstrap filter. In addition, since the majority of the signal processing efficiently resides on the fusion center, the computation load at the sensor nodes is limited, which is desirable for power-aware systems. Last, the performance of the new tracking algorithm in various tracking scenarios is thoroughly studied and compared with standard tracking methods. As shown in the theory and demonstrated by our experimental results, the state-partition-based centralized particle filter reliably outperforms the traditional method in all experiments. </para>

Wireless Sensor Network (WSN) is a new technology integrated with communications, embedded technology, and network functions; having capability of real-time collecting, communicating, and sensing, for which dynamic target locating and tracking is its critical task, as well as a basic element in its commercial and military application such as battlefield information accessing and traffic monitoring. Sensor target tracking is the technology of target location estimate based on measurements from corresponding sensors. An inaccurate estimated location may very easily lead to target tracking failure which reduces the reliability and stability of the target tracking [1]. Experts and scholars have done a lot of researches to solve problems exited in wireless sensor target tracking, and many new tracking algorithms have been proposed, among which tracking algorithms such as target contrail-fitting, linear prediction, Kalman filter, unscented Kalman filter(UKF) and particle filter[2] are most common applied. Sikdar, with his partners, has proposed target location tracking algorithm based on linear sensor which is proved better than conventional contrail fitting algorithm in its effects. Zhang Xiaoping , with his partners, has proposed WSN target tracking algorithm through modeling for quadratic polynomial motions which has improved target tracking accuracy[3]. However, there is a problem in common: all the algorithms above assume the tracking target contrail-fittings being in linear motion, while the actual contrail fitting is of uncertainty and variability, and the estimated accuracy can hardly be improved after being to a certain level. In this condition, the tracking error of the above algorithms can be greater [4]. Kalman filter algorithm can be applied for weakly nonlinear models, but for strongly nonlinear models, Unscented Kalman Filter algorithm can be better. Kalman filter work well in cases based on model-linear and Gaussian noise, thus its estimated accuracy is not that ideal [5, 6]. Particle filter algorithm is based on optimal regression Bayesian filtering algorithm in Monte Carlo simulation, which can accurately estimate the any distribution state with only a small amount of mobile anchor nodes. PF algorithm has distinguished advantages in processing non-linear, non-Gaussian cases [7]. However, in PF application process after several iterations, diversified degradation of the particles will occur, which is likely to lead a target tracking loss, thus further affect WSN target tracking accuracy [8]. In order to solve the diversified degradation of the particles in Particle Filter algorithm, this paper Research on the Target Tracking Algorithm for Wireless Sensor Network Based on Improved Particle Filter Liang Peng,Xinhan Huang, Li He

Vehicle detection and tracking from a UAV perspective often encounters omission and misdetection due to the small targets, complex scenes and target occlusion, which finally influences hugely on detection accuracy and target tracking stability. Additionally, the number of parameters of current model is large that makes it is hard to be deployed on mobile devices. Therefore, this paper proposes a YOLO-LMP and NGCTrack-based target detection and tracking algorithm to address these issues. Firstly, the performance of detecting small targets in occluded scenes is enhanced by adding a MODConv to the small-target detection head and increasing its size; In addition, excessive deletion of prediction boxes is prevented by utilizing LSKAttention mechanism to adaptively adjust the target sensing field at the downsampling stage and combining it with the Soft-NMS strategy. Furthermore, the C2f module is replaced by the FPW to reduce the pointless computation and memory utilization of the model. At the target tracking stage, the so-called NGCTrack in our algorithm replaces IOU with GIOU and employs a modified NSA Kalman filter to adjust the state-space aspect ratio for width prediction. Finally, the camera adjustment mechanism was introduced to improve the precision and consistency of tracking. The experimental results show that, compared to YOLOv8, the YOLO-LMP model improves map50 and map50:95 metrics by 10.3 and 12.2%, respectively and the number of parameters is decreased by 47.7%. After combined it with the improved NGCTrack, the number of IDSW reduced by 73.6% compared to the ByteTrack method, while the MOTA and IDF1 increase by 5.2 and 9.8%, respectively.

In the paper, we devise and evaluate a fully decentralized, light-weight, dynamic clustering algorithm for target tracking. Instead of assuming the same role for all the sensors, we envision a hierarchical sensor network that is composed of (a) a static backbone of sparsely placed high-capability sensors which assume the role of a cluster head (CH) upon triggered by certain signal events; and (b) moderately to densely populated low-end sensors whose function is to provide sensor information to CHs upon request. A cluster is formed and a CH becomes active, when the acoustic signal strength detected by the CH exceeds a pre-determined threshold. The active CH then broadcasts an information solicitation packet, asking sensors in its vicinity to join the cluster and provide their sensing information. We address and devise solution approaches (with the use of Voronoi diagram) to realize dynamic clustering: (I1) how CHs cooperate with one another to ensure that for the most of time only one CH (preferably the CH that is closest to the target) is active; (I2) when the active CH solicits for sensor information, instead of having all the sensors in its vicinity reply, only a sufficient number of sensors respond with non-redundant, essential information to determine the target location; and (I3) both packets with which sensors respond to their CHs and packets that CHs report to subscribers do not incur significant collision. Through both probabilistic analysis and ns-2 simulation, we show with the use of Voronoi diagram, the CH that is usually closest to the target is (implicitly) selected as the leader and that the proposed dynamic clustering algorithm effectively eliminates contention among sensors and renders more accurate estimates of target locations as a result of better quality data collected and less collision incurred.

Target tracking is a hot topic in the field of computer vision. It uses the context information of video or image sequence to model the appearance and motion information of the target, so as to predict the motion state of the target and calibrate the position of the target. From the perspective of building models, target tracking algorithms can be divided into generative models and discriminant models; From the number of tracking targets, it can be divided into single target tracking and multi-target tracking. Target tracking integrates the theories and algorithms of image processing, machine learning, optimization and other fields. It is the premise and foundation to complete higher-level image understanding.The basic task of target tracking is to give the initial position of the target in a video sequence and continuously track and locate the target in each subsequent frame. In this process, a priori conditions such as the color, shape and size of the target will not be provided, that is, the tracking algorithm can track only by learning the target in the first frame. The traditional solution is to calculate the project similarity offline and save it in the system for algorithm call, but it can not make full use of the latest scoring data to reflect the change of user interest. To solve the above problems, a target tracking item similarity incremental update mechanism suitable for online applications is proposed, so that the recommendation system can update the similarity data between the corresponding item and other items in real time after the current user submits the item score, so that the recommendation system can recommend based on the latest item similarity data, To adapt to changes in user interests.

An important application of sensor networks is target tracking and localization. To deal with sensor nodes with limited energy supply and communication band width we propose energy-efficient hierarchical architectures for solving the target tracking problem. In these networks, sensors form clusters and transmit minimal quantized information about a sensed event to a specialized node, known as a cluster head. Cluster heads are equipped with capability of communicating over large distances with a fusion center or a base station. We consider two different hierarchical architectures : (a) the target dynamics are probabilistically estimated at the cluster heads and their statistics combined at the fusion center, and (b) the cluster heads perform simple compression rules on the quantized sensor data and the fusion center estimates the target dynamics using these severely compressed data. Sequential Monte Carlo algorithms for estimation of the target dynamics are used. Through computer simulations the performances of these two architectures are studied.

In this paper, the research significance of infrared small target detection and tracking is briefly introduced. Secondly, the target detection and tracking algorithm is reviewed. The basic concepts and main methods of target detection and tracking technology are clarified. The deep learning algorithm is introduced, because it becomes more and more popular in target detection and tracking algorithm. Low and relatively complex background, the detection and tracking of infrared small targets is difficult and important.

Feature points and object edges are two kinds of primitives which are frequently used in target tracking algorithms. Feature points can be easily localized in an image. Their correspondences between images can be detected accurately. They can adapt to wide baseline transformations. However, feature points are not so stable that they are fragile to changes in illumination and viewpoint. On the contrary, object edges are stable under a very wide range of illumination and viewpoint changes. Unfortunately, edge-based algorithms often fail in the presence of highly textured targets and clutter which produce too many irrelevant edges. We found that both edge-based and point-based tracking have failure modes which are complementary. Based on this analysis, we propose a novel tracking algorithm which fuses point and edge features. Our tracking algorithm uses feature points matching to track object first, and then uses the transformation parameters archived in the first step to initialize the edge tracking. By this means, our algorithm alleviates the disturbance of irrelevant edges. Then, we use the texture boundary detection algorithm to find the precise object boundary. Texture boundary detection is different from the conventional gradient-based edge detection which can directly compute the most probable location of a texture boundary on the search line. Therefore, it is very fast and can be incorporated into a real-time tracking algorithm. Experimental results show that our tracking algorithm has outstanding tracking accuracy and robustness.

Target positioning and tracking in wireless sensor networks (WSNs) are necessary for real ap-plications. This paper uses an artificial fish swarm algorithm (AFSA) with receive signal strength indicator (RSSI) for indoor target positioning and tracking. The performance of random and fixed artificial fish deployment topologies in AFSA for target positioning and tracking is investigated when the number of artificial fish is 12, 24, 52, 72, and 100, respectively. The simulation results show that the difference in average error between the random and fixed deployments is 5%, and the average positioning time is 2% for target position. The target tracking performance of fixed deployment is better than that of random one when the number of artificial fish is small.

This paper aims to increase the Unmanned Aerial Vehicle's (UAV) capacity for target tracking. First, a control model based on fuzzy logic is created, which modifies the UAV's flight attitude in response to the target's motion status and changes in the surrounding environment. Then, an edge computing-based target tracking framework is created. By deploying edge devices around the UAV, the calculation of target recognition and position prediction is transferred from the central processing unit to the edge nodes. Finally, the latest Vision Transformer model is adopted for target recognition, the image is divided into uniform blocks, and then the attention mechanism is used to capture the relationship between different blocks to realize real-time image analysis. To anticipate the position, the particle filter algorithm is used with historical data and sensor inputs to produce a high-precision estimate of the target position. The experimental results in different scenes show that the average target capture time of the algorithm based on fuzzy logic control is shortened by 20% compared with the traditional proportional-integral-derivative (PID) method, from 5.2 s of the traditional PID to 4.2 s. The average tracking error is reduced by 15%, from 0.8 m of traditional PID to 0.68 m. Meanwhile, in the case of environmental change and target motion change, this algorithm shows better robustness, and the fluctuation range of tracking error is only half of that of traditional PID. This shows that the fuzzy logic control theory is successfully applied to the UAV target tracking field, which proves the effectiveness of this method in improving the target tracking performance.

The underwater sensor network is currently a hot research field in academia and industry with many underwater applications, such as ocean monitoring, seismic monitoring, environment monitoring, and seabed exploration. Underwater target tracking is a critical component of ocean development. This paper studies the underwater target tracking problem of the wireless sensor network. The core technology of the target tracking algorithm is the filtering algorithm, which identifies the accuracy of the target tracking system. Nonlinear filtering is a hot issue in target tracking because feasible projects are mostly non-linear systems. The linearization method used in traditional Kalman filtering has serious shortcomings. Therefore, this paper presents the improved cubature Kalman filtering (ICKF) algorithm for underwater target tracking. There is uncertainty in the target movement, an adaptive forgetting factor is given into the cubature Kalman filtering algorithm to directly modify the error covariance to reduce the impact of uncertainties. Then, interactive multi-model technology is introduced to establish the IMMICKF algorithm with multiple states. Compared with other filtering algorithms, the new algorithm can effectively deal with non-linear target tracking problems and obtain better estimation accuracy. The numerical simulation is given to demonstrate the effectiveness of the IMMICKF algorithm.

The target tracking by space-based surveillance systems is difficult due to the long distances, weak energies, fast speeds, high false alarm rates, and low algorithmic efficiencies involved in the process. To mitigate the impact of these difficulties, this article proposes a target tracking algorithm based on image processing and Transformer, which employs a two-dimensional Gaussian soft-thresholding method to reduce the image noise, and combines a Laplace operator-weighted fusion method to augment the image, so as to improve the overall quality of the image and increase the accuracy of target tracking. Based on the SiamCAR framework, the Transformer model in the field of natural language processing is introduced, which can be used to enhance the image features extracted from the backbone network by mining the rich temporal information between the initial and dynamic templates. In order to capture the information of the target’s appearance change in the temporal sequence, a template update branch is introduced at the input of the algorithm, which realizes the dynamic update of the templates by constructing a template memory pool, and selecting the best templates for the candidate templates in the memory pool using the cosine similarity-based selection, thus ensuring the robustness of the tracking algorithm. The experimental results that compared with the SiamCAR algorithm and the mainstream algorithms, the TrD-Siam algorithm proposed in this article effectively improves the tracking success rate and accuracy, addressing poor target tracking performance under space-based conditions, and has a good value of application in the field of optoelectronic detection.

Target detection and tracking in real-time video streaming is one of the core tasks in the field of computer vision, and its performance directly affects the effectiveness of intelligent surveillance, automatic driving and other applications. In this paper, a three-frame frame difference method incorporating Sobel operator is proposed to optimize the boundary extraction by combining morphological filtering. Meanwhile, the Mean Shift-based target tracking algorithm is improved by introducing a recognizable interferer model with a dual attention mechanism to enhance the tracking robustness. Experiments show that the improved inter-frame difference method achieves F-measure of 93.38%, 96.20% and 91.83% on Highway, Road and Highway2 datasets, respectively, and the detection frame rate is improved to 32 f/s. The improved Mean Shift algorithm improves the tracking accuracy by 2.67%-3.09% and the success rate by 0.98%-1.75% over the mainstream algorithm on the UAV123 and VOT2024 datasets. In addition, the multi-target tracking experiments achieve MOTA of 75.76% and 80.61% on the MOT16 and MOT17 datasets, respectively, validating the comprehensive performance advantages of the algorithms. This study provides an efficient and robust solution for target detection and tracking in real-time video streaming.

Target tracking is a typical application in wireless sensor networks. Both energy efficiency and tracking performance are important issues that need to be considered. They are a pair of contradictions most of the time. Saving energy often sacrifices tracking performance, while enhancing tracking performance needs to consume more energy. In this paper, an efficient sleep scheduling algorithm is put forward to tackle the above problem in energy harvesting sensor networks. At first, we modify the probability-based prediction and sleep scheduling (PPSS) algorithm to track the target and further use another sleep scheduling algorithm we proposed to wake tracking nodes when the target is likely to be missed (i.e., it is unsuccessful to wake next-moment tracking nodes). Secondly, a double-storage energy harvesting architecture is employed to increase residual energy of sensor nodes and to extend network lifetime. Simulation results reveal that the proposed sleep scheduling algorithm can improve tracking performance and prolong network lifetime compared with the PPSS algorithm and the proposed algorithm without energy harvesting.