Adaptive Target Tracking Algorithm Based on Template Updating and Graph-Regularized Saliency

Visual tracking has been widely used in robot systems, and numerous approaches for visual tracking have been proposed. However, developing a robust and real-time visual tracking algorithm which can adaptively track the varying appearance of target under challenging conditions for mobile robot is still an open problem. This paper presents an adaptive probabilistic tracking algorithm with multiple cues integration. An effective evaluation function is proposed to evaluate each cue used for tracking based on their discriminating abilities between foreground and background. Then the likelihood functions of the cues are integrated in particle filter framework with different weights determined based on the evaluation scores. A novel target model updating strategy is proposed to adapt to the varying appearance of target resisting gradual drift which is still an unsolved problem in many adaptive tracking algorithms. Experimental results on a mobile robot demonstrate the robust performance of the proposed algorithm under challenging conditions.

The receiver of a global navigation satellite system (GNSS) is likely to lose tracking for a GNSS signal in some degraded environments. To solve this problem, this paper designs a new adaptive tracking algorithm for GNSS signals. First, we design an error extraction module to extract phase errors so that a CTL can estimate phase errors without using a loop discriminator, which can reduce the requirements of the carrier-to-noise ratio (CNR) of GNSS signals. Second, we design a motion detection module to detect the real-time movement status of GNSS receivers. Then, using its detecting results as inputs, we design an algorithm to automatically make the CTL switch between a second-order loop and a third-order loop. Third, we design a bandwidth-adjusting module to adjust the bandwidth of CTL, according to the CNR and the movement status of a GNSS receiver. Finally, a simulation is performed to verify that our adaptive carrier tracking algorithm can effectively improve the precision of CTL, as well as enhance its dynamic range.

The UWB through-wall radar plays an unique role in disaster rescue operation, by providing the identification and location of the on-site survivors under fire, earthquake, collapse and other disaster conditions, as well as anti-terrorism and explosive ordnance disposal scenarios. However, the scenario of the disaster site may be complex, and the internal structure and furnishings inside the building could not be predicted in advance. A special scene with metal obstacles in the detection space is investigated in this paper. The UWB through wall radar test system and its composition is presented, and then the algorithms for human target adaptive motion filter detection and obstacle interference suppression are discussed. Finally, a tracking trace aggregation algorithm for strong offset targets is proposed. By analyzing the test data., the results show that the adaptive target detection method proposed in this paper could effectively eliminate the interference of metal obstacles, achieving enough signal-to-noise ratio and signal-to-clutter ratio. The improved point trace aggregation algorithm is also verified in target tracking test, achieving ideal tracking and detection results are obtained.

In order to improve the tracking accuracy of surface target with multiple cruise missiles, an adaptive fusion tracking algorithm based on multi-missile network is proposed. For this method, optimized arrangement of chief missiles and attack missiles is performed to improve the penetration capability of multiple cruise missiles firstly. Then time alignment and space alignment is adopted to reduce the difference multiple cruise missiles. Finally, adaptive fusion tracking algorithm is performed to further improve the tracking accuracy of multiple cruise missiles based on the statistic threshold metric. Simulation results show that, target tracking can be finished more effectively than existing methods by using the proposed algorithm.

An adaptive template matching-based single object tracking algorithm with parallel acceleration

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.

In complex scenes with light changes, deformations, and occlusions, target tracking easily contains a large amount of background color information when building a target color model. Thus, the tracking effect is reduced. To improve the accuracy of the traditional continuously adaptive mean-shift algorithm (CAMShift) in complex scenarios, a target tracking algorithm based on an improved Gaussian mixture model was proposed. Using the Gaussian mixture model, the tracking image was divided into the foreground and background superposition. The histograms of the hue component were respectively established in the foreground and background of the target area. By suppressing the same hue as the background color in the tracking image, the target color model was established. The target position was iteratively obtained by implementing the CAMShift algorithm using the enhanced target color model. The Bhattacharyya distance between the candidate target and the target template was used as basis for updating the target model. Simulation analysis under benchmark data sets and actual monitoring scenarios verified the accuracy of the proposed algorithm. Results show that the distance precision and overlap success rate of the proposed algorithm are 0.88 and 0.625, respectively. The proposed algorithm effectively solves long-term target tracking problems with complex scenes, such as occlusion, background clutters, and illumination variation. This study eliminates the problem of target recognition caused by environmental changes and provides references for real-time monitoring of abnormal traffic conditions.

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

Current research develops a target-tracking framework that in turn enables a visionbased surveillance system concept suitable for airport ramp area operations. The surveillance approach is based on detecting and tracking aircraft only using cameras. A multiple target tracking algorithm based on nearest neighbor standard filter is used in the research. The measurement model consists of the position and velocities of the aircraft. The aircraft positions are obtained from an object detection algorithm and the velocities from optical flow estimation. Data association between measurements and targets is achieved by solving a linear assignment problem. An innovative aspect of this research involves the usage of Hausdorff metric to quantify the distances between established targets and measurements. A linear model with additive Gaussian noise is assumed for target propagation and Kalman-filter is used to remove noise and localize the target. Moreover, a novel methodology for target death and birth is proposed which is consistent with the current target tracking framework. We apply the proposed multiple-target tracking algorithm to track aircraft in a cluttered ramp area environment of an airport. It is observed that the target tracking algorithm is able to consistently track aircraft through different frames.

In this paper an advanced anti-jam indoor adaptive GNSS signal acquisition and tracking algorithm is considered. Initially, we were able to determine that double-dwell structure (DDS) reduces the processing time penalty caused by false alarm. Nevertheless, the double-dwell structure is still vulnerable to interference and jamming. In order to determine a suitable advanced anti-jam indoor adaptive GNSS signal acquisition and tracking algorithm for DDS we first perform the Bayesian parameter estimation; i.e., we analytically compute the posterior Bayes probability density function (pdf) and cumulative distribution function (cdf) by applying the Bayes theorem in three steps. First, we compute the complex signal distribution and complex matrix variate signal distribution. This is an original new result never published before. Second, we provide an introduction of the complex Bessel or parabolic function interference distribution due to earlier assumptions of the DDS interference distribution models. We further explain that the process that is required to produce Complex Matrix Variate Bessel Interference Distribution or Complex Matrix Variate Parabolic Function Interference Distribution is an extremely daunting task let alone the Complex Matrix Variate DDS Bayesian posterior density it would seem at the moment nearly an impossible task because it may require the computation of functions such as Kampe de Feriet function and Jack functions of matrix arguments. Third, instead of performing the second step, we relax the assumption of interference to normal distribution. In both the scalar case and complex matrix variate cases we observe that the complex matrix variate Bayesian posterior pdf or cdf is invariant of the observation data or is identical to the prior complex matrix variate signal distribution model. This is an original new and very powerful result never published before. Why this result is so powerful is because up until now we never had a complete theoretical validation of our GNSS receiver design based on either autocorrelation or cross-correlation properties since, the complex matrix variate Bayesian posterior pdf or cdf is invariant of the observation data or is identical to the prior complex matrix variate signal distribution model. Future simulation results will illustrate that an anti-jam indoor adaptive DDS stable detection structure reduces by half the average acquisition time and significantly outperform its predecessor against interference and jamming. Moreover, future simulation results will show further advancements of the Giftet Inc. MATLAB library capability to perform advanced numerical computations based on closed form expressions of the generalized Bessel function distributions and generalized parabolic cylinder function distribution models via Kampe de Feriet function and Jack functions.

Aiming at the problem that the traditional correlation filter tracking algorithm is prone to tracking failure under the target’s scale change and occlusion environment, we propose a scale-adaptive Kernel Correlation Filter (KCF) target tracking algorithm combined with the learning rate adjustment. Firstly, we use the KCF to obtain the initial position of the target, and then adopt a low-complexity scale estimation scheme to get the target's scale, which improves the ability of the proposed algorithm to adapt to the change of the target's scale, and the tracking speed is also ensured. Finally, we use the average difference between two adjacent images to analyze the change of the image, and adjust the learning rate of the target model in segments according to the average difference to solve the tracking failure problem when the target is severely obstructed. Compared the proposed algorithm with other five classic target tracking algorithms, the experimental results show that the proposed algorithm is well adapted to the complex environment such as target’s scale change, severe occlusion and background interference. At the same time, it has a real-time tracking speed of 231 frame/s.

Target detection and tracking important in many applications including intelligent monitoring system, defense system and terminal guidance system. Aiming to solve the problem of simulated target tracking, this paper proposes an adaptive algorithm which uses the fusion of the spectral and morphological features of multispectral image to realize the target tracking based on the Particle Filter. Firstly, the target area is manually initialized in the multispectral image and the spectral and texture features of the target are extracted. Secondly, we build the adaptive tracking model of multiple features under the framework of Particle Filter. We validate the effectiveness of the proposed approach on the MATLAB platform. The results show that the proposed approach achieves accurate and stable multispectral target tracking in complex scenes by improving the efficiency of particles usage under defective tracking conditions, which is of great theoretical and practical values for the application of multispectral target tracking technology.

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.

The aim of this paper is to propose, develop and test multi-radar multi-target tracking algorithm for maritime surveillance at over-the-horizon (OTH) distances. The proposed algorithm utilizes a weighted pure minimum mean square error (MMSE) algorithm for multi-radar data fusion and adaptive alpha — beta tracking algorithm for target tracking. The developed data fusion algorithm calculates the weighted center of mass of all targets observed by OTH radars (OTHRs) within a gate radius of predicted position, and uses it for a track update process. During the process a weighting factor which will be used in an adaptive alpha-beta tracking algorithm is derived. The weighting factor as well as alpha and beta factors are updated in each observation period and depend on all targets, which have fallen into a gate radius during the track update process. The algorithm is designed, implemented, and tested in an environment consisting of a simulated network of two OTHRs with significantly overlapping surveillance areas.

For the tracking problem of moving target with scale changes, an adaptive bandwidth tracking algorithm based on principal component variation detection of histogram is proposed. Considering feature characteristics of target and background, such as conspicuousness and similarity, discrimination is estimated to select the feature which can differentiate the target and its background to its extent. The number of feature bins is determined by the discrimination. Target scale variation is analyzed by the number of pixels of principal component in histogram. A novel bandwidth criterion is defined to adjust the tracking window adaptively. Simulation shows that the algorithm can detect the scale change of the target and track the target more accurately.