Robust Tracking Method Research Articles

Passivity-based adaptive trajectory control of an underactuated 3-DOF overhead crane

This paper presents a robust passivity-based adaptive control method for payload trajectory tracking of a three degree-of-freedom overhead crane with a varying-length rigid hoist cable. A reformulation of the overhead crane’s equations of motion is used and an adaptive control law is developed to ensure the system has an output strictly passive (OSP) input–output mapping. The Passivity Theorem is used to prove that the closed-loop system is input–output stable when an OSP negative feedback controller is implemented. Convergence and boundedness of the payload trajectory tracking error is proven with two candidate OSP feedback controllers, including a constant gain and a strictly positive real (SPR) controller. Numerical simulations and experiments are performed with the proposed control method and two state-of-the-art control laws from the literature. The results demonstrate the practicality of the proposed control method and the improved performance of the proposed SPR controller in the experiments compared to the controllers from the literature.

V-DaT: A Robust method for Vehicle Detection and Tracking

Vision-based traffic surveillance has been one of the most promising fields for improvement and research. Still, many challenging problems remain unsolved, such as addressing vehicle occlusions and reducing false detection. In this work, a method for vehicle detection and tracking is proposed. The proposed model considers background subtraction concept for moving vehicle detection but unlike conventional approaches, here numerous algorithmic optimization approaches have been applied such as multi-directional filtering and fusion based background subtraction, thresholding, directional filtering and morphological operations for moving vehicle detection. In addition, blob analysis and adaptive bounding box is used for Detection and Tracking. The Performance of Proposed work is measured on Standard Dataset and results are encouraging.

Establishing Robust Feature Point Detection and Tracking Methods for Face Orientation

Establishing Robust Feature Point Detection and Tracking Methods for Face Orientation

Aberrance suppressed spatio-temporal correlation filters for visual object tracking

The objective of the present study is to design a correlation filter-based tracking method for robust visual object tracking. In the literature, numerous tracking methods have been proposed based on discriminative correlation filter (DCF) and obtained impressive performance. However, existing algorithms still face difficulties such as partial occlusion, clutter background, uncertainties, boundary effects (especially when the target search area is small) and other challenging visual factors. Furthermore, during the target detection process, the sudden changes in objects caused by illumination variations and partial/full occlusion degrade the performance. To tackle the drawbacks mentioned earlier, we propose a tracking algorithm concerning the aberrance suppressed correlation filters with spatio-temporal information for visual tracking. Specifically, we introduce a spatial regularization term into the correlation filter to suppresses the boundary effects. Following that, a temporal regularization is adopted into the DCF-based framework to achieve a more robust appearance model and further enhance the tracking performance. In addition, we introduce an approach to suppress the aberrance in response maps caused by the sudden changes. Technically, our proposed method can be directly solved by using the alternating direction method of multipliers (ADMM) technique with a low computational cost. Finally, extensive experimental results on OTB2013, OTB2015, TempleColor128 and UAV123 datasets demonstrate that the proposed method performs favorably against state-of-the-art methods.

Robust Monocular Pose Tracking of Less-Distinct Objects Based on Contour-Part Model

Due to the lack of distinctiveness to the background, robust monocular 6DOF pose tracking of less-distinct objects remains an important open problem. In this paper, we firstly analyze the object distinctiveness in the tracking process. Then, we propose a novel contour-part model based robust monocular pose tracking method for less-distinct objects. This paper uses the traditional contour feature for pose tracking in a novel strategy called contour part model. First, the contour part model is built by segmenting the projected contour rendered from the 3D model into contour segments of a certain length adaptively according to the Shi-Tomasi cornerness scores. Then, the correspondence is detected in the input image for each contour part by gradient orientation based template matching. Finally, pose tracking is achieved by solving the PnP problem. Experiment results on semi-synthetic and real images show that the proposed method performs better than the existing methods when pose tracking less-distinct objects and shows great robustness toward interference. Additionally, we combine the edge feature and the regional feature in a simple strategy. The overall performance of the fused method is boosted according to the experimental results on the RBOT dataset. The fact shows that the fusion of the edge and regional features has great potential for improving tracking performance.

Advances in our understanding and distribution of the Cronobacter genus in China.

This review considers how research in China has progressed our understanding and subsequent improved control of Cronobacter. This emergent bacterial pathogen is associated with neonatal infections through the ingestion of contaminated prepared feed. The review includes large-scale surveys of various sources of the organism, including infant formula production facilities. The analysis of over 20,000 samples is presented. Over 10,000 being from powdered infant formula and other infant foods as well as environmental sampling of production facilities, the remaining being from food, food ingredients, and human carriage. A major advance in China was adopting DNA-sequence-based methods (that is, multilocus sequence typing, clustered regularly interspaced short palindromic repeats-cas array profiling, and single-nucleotide polymorphism analysis) for the identification and genotyping of the organism. These methods have considerably advanced our understanding of the taxonomy, ecology, and virulence of this organism. In turn, this has improved source tracking of the organism both in infant formula production facilities and epidemiological investigations. Furthermore, whole-genome sequencing has revealed a range of virulence and persistence mechanisms as well as plasmid-borne multidrug resistance traits. China now has reliable and robust methods for accurate microbial source tracking of Cronobacter for use both in the food production environment and epidemiological analysis.

Robust Detection and Tracking Method for Moving Object Based on Radar and Camera Data Fusion

Obtaining the accurate and real-time state of surrounding objects is essential for automated vehicle planning and decision-making to ensure safe driving. In complex traffic scenarios, object occlusion, clutter interference, and limited sensor detection capabilities lead to false alarms and missed object detection, making it challenging to ensure the stability of tracking and state prediction. To address these challenges, in this study, we developed a robust multi-object detection and tracking method for moving objects based on radar and camera data fusion. First, the radar and camera perform target detection independently, and the detection results are correlated in the image plane to generate a random finite set with an object type. Then, based on the Gaussian mixture probability hypothesis density algorithm framework, the tracking process is improved using elliptic discriminant thresholds, an attenuation function, and simplified pruning methods. The experimental results demonstrate that the improved algorithm can accurately estimate the number and state of targets in object occlusion, measurement loss scenarios, and achieve robust continuous multi-object tracking. The proposed method could guide the design of safer and more efficient intelligent driving systems.

A Fast and Robust Seam Tracking Method for Spatial Circular Weld Based on Laser Visual Sensor

Spatial circular welding is an important application in industrial production. However, some feature points extraction methods for spatial circular welds have disadvantages such as large amounts of calculation and poor anti-interference ability. In addition, due to the welding thermal deformation, the spatial circular weld needs to be tracked. Therefore, a fast and robust seam tracking method for spatial circular weld based on laser visual sensors is proposed in this article. First of all, a self-updating template matching (SUTM) method is proposed to get weld feature points. Compared with the method based on geometric features, the proposed SUTM method has the advantages of rapidity and robustness. What is more, an algorithm for spatial circle center fitting based on random sampling consensus (RANSAC) is proposed, which overcomes the interference of outlier points and improves the robustness of circle center detection. At the same time, the tracking error model of the spatial circular weld is established and the welding deviation from the $x$ -, $y$ -, and $z$ -directions is obtained. By correcting the deviation in real-time, the fast and robust real-time seam tracking of the spatial circular weld is realized. Through welding test, the proposed method can obtain the position of the spatial fillet circular weld quickly and robustly and complete the high-precision seam tracking.

Tracking Longitudinal Population Dynamics of Single Neuronal Calcium Signal Using SCOUT

In vivo calcium imaging enables simultaneous recording of large neuronal ensembles engaged in complex operations. Many experiments require monitoring and identification of cell populations across multiple sessions. Population cell tracking across multiple sessions is complicated by non-rigid transformations induced by cell movement and imaging field shifts. We introduce SCOUT (Single-Cell SpatiOtemporal LongitUdinal Tracking), a fast, robust cell tracking method utilizing multiple cell-cell similarity metrics, probabilistic inference, and an adaptive clustering methodology to perform cell identification across multiple sessions. By comparing SCOUT with current popular cell tracking algorithms on simulated, 1-photon and 2-photon recordings, we empirically show this approach improves cell tracking quality, particularly when recordings exhibit spatial footprint movement between sessions or poor neural extraction quality.

Application of bioluminescence resonance energy transfer-based cell tracking approach in bone tissue engineering

Bioluminescent imaging (BLI) has emerged as a popular in vivo tracking modality in bone regeneration studies stemming from its clear advantages: non-invasive, real-time, and inexpensive. We recently adopted bioluminescence resonance energy transfer (BRET) principle to improve BLI cell tracking and generated the brightest bioluminescent signal known to date, which thus enables more sensitive real-time cell tracking at deep tissue level. In the present study, we brought BRET-based cell tracking strategy into the field of bone tissue engineering for the first time. We labeled rat mesenchymal stem cells (rMSCs) with our in-house BRET-based GpNLuc reporter and evaluated the cell tracking efficacy both in vitro and in vivo. In scaffold-free spheroid 3D culture system, using BRET-based GpNLuc labeling resulted in significantly better correlation to cell numbers than a fluorescence based approach. In scaffold-based 3D culture system, GpNLuc-rMSCs displayed robust bioluminescence signals with minimal background noise. Furthermore, a tight correlation between BLI signal and cell number highlighted the robust reliability of using BRET-based BLI. In calvarial critical sized defect model, robust signal and the consistency in cell survival evaluation collectively supported BRET-based GpNLuc labeling as a reliable approach for non-invasively tracking MSC. In summary, BRET-based GpNLuc labeling is a robust, reliable, and inexpensive real-time cell tracking method, which offers a promising direction for the technological innovation of BLI and even non-invasive tracking systems, in the field of bone tissue engineering.

An improved object tracking algorithm based on adaptive weighted strategy and occlusion detection mechanism

Due to complex background and volatile object shape-appearance in image, the stability and accuracy of tracking algorithm is often disturbed and reduced. So how to accurately and robustly track object in object tracking application is a challenge topic at home and abroad. Built upon the methodologies of compressive tracking and spatio-temporal context, a simple yet robust object tracking method is proposed for solving the drift and occlusion problems in paper. It combines two existing classical ideas into a single framework: adaptive weighted idea and occlusion detection mechanism. In order to weaken interference problems of object background, object area is firstly partitioned into equal-sized sub-patches and the different weight related with location information is assigned for each patch; Then, for improving its robustness, Bhattacharyya distance is adopted to find out these samples with maximum discrimination; In addition, our proposed occlusion detection mechanism is for recapturing the tracked object when occlusion occurs. Many simulation experiments show that our proposed algorithm achieves more favorable performance than these existing state-of-the-art algorithms in handing various challenging infrared videos, especially occlusion and shape deformation.

Motion tracking of left myocardium in cardiac cine magnetic resonance image based on displacement flow U-Net and variational autoencoder

The high morbidity and mortality of cardiovascular diseases (CVDs) seriously affects the quality of human life. How to asses cardiac function, assist in the diagnosis and treatment of clinical CVDs and evaluate prognosis is a problem to be solved urgently. In response to this issue, based on previous work of Cardiac Cine Magnetic Resonance (CCMR) image segmentation of the left myocardium (LVM), a robust and accurate LVM motion tracking method (DispFlow_UNet_Flow) with using the displacement flow UNet (DispFlow_UNet) and biomechanics-informed variational autoencoder (VAE) is proposed in this paper. The following are the main research contents: (1) building a compressed excitation residual U-net network to accurately segment LVM, calculating the ventricular volume and myocardial mass according to the segmentation results, and then evaluating the overall cardiac function; (2) reconstructing the dense displacement field (DDF) based on the proposed motion tracking method, and obtaining the LVM dense displacement field by combining the LVM segmentation mask; (3) contrasting and evaluating the motion tracking results by using the true displacement vector field of simulated data and clinical data sets. All the results show that the tracking algorithm proposed in this paper has high precision and generalization capability.

A neural adaptive level set method for wildland forest fire tracking

Tracking of smoke and fire in videos can provide helpful regional measures to evaluate precisely damages caused by fires. In security applications, real-time video segmentation of both fire and smoke regions represents a crucial operation to avoid disaster. In this paper, we propose a robust tracking method for fire regions in forest wildfire videos using neural pixel classification approach combined with a non-linear adaptive level set method based on the Bayesian rule. Firstly, an estimation function is built with chromatic and statistical features using linear discriminant analysis and a trained multilayer neural network in order to get a preliminary fire localisation in each frame. This function is used to compute initial curve and the level set evolution parameters providing fast refined fire segmentation in each processed frame. The experimental results of the proposed method prove its accuracy and robustness when tested on different varieties of wildfire-smoke scenarios.

Deep Siamese Cross-Residual Learning for Robust Visual Tracking

The sixth-generation (6G) wireless technology contributes to the establishment of the Internet of Things (IoT). Recently, the IoT has become popular because of its smart architectures and various applications. Among these applications, intelligent urban surveillance systems for smart cities are becoming more and more important. Therefore, designing a robust visual tracking method has become an urgent task. Deep Siamese convolutional neural networks have been applied to visual tracking recently because of their advantageous abilities to learn a matching function between the template and the target candidate. Unlike traditional Siamese networks, which separately treat the two branches, we propose deep Siamese cross-residual learning to entangle the two branches from the beginning to the end of the Siamese network. This strategy can make the two branches exchange instance-specific information at different nodes of the network and learn a more compact representation of the target. In addition, we propose a combined loss function, which consists of two complementary tasks. One task is to learn a matching function directly and the other one is to learn a classification function. Moreover, our model does not need to load any pretrained weights and is trained with limited sequences from scratch. Plenty of experiments show that our tracker performs favorably against many state-of-the-art tracking methods.

Event-Based Object Detection and Tracking for Space Situational Awareness

In this work, we present an optical space imaging dataset using a range of event-based neuromorphic vision sensors. The unique method of operation of event-based sensors makes them ideal for space situational awareness (SSA) applications due to the sparseness inherent in space imaging data. These sensors offer significantly lower bandwidth and power requirements making them particularly well suited for use in remote locations and space-based platforms. We present the first publicly-accessible event-based space imaging dataset including recordings using sensors from multiple providers, greatly lowering the barrier to entry for other researchers given the scarcity of such sensors and the expertise required to operate them for SSA applications. The dataset contains both day time and night time recordings, including simultaneous co-collections from different event-based sensors. Recorded at a remote site, and containing 572 labeled targets with a wide range of sizes, trajectories, and signal-to-noise ratios, this real-world event-based dataset represents a challenging detection and tracking task that is not readily solved using previously proposed methods. We propose a highly optimized and robust feature-based detection and tracking method, designed specifically for SSA applications, and implemented via a cascade of increasingly selective event filters. These filters rapidly isolate events associated with space objects, maintaining the high temporal resolution of the sensors. The results from this simple yet highly optimized algorithm on the space imaging dataset demonstrate robust high-speed event-based detection and tracking which can readily be implemented on sensor platforms in space as well as terrestrial environments.

A robust iterative pose tracking method assisted by modified visual odometer

Purpose The purpose of this paper is to propose a robust iterative LIDAR-based pose tracking method assisted by modified visual odometer to resist initial value disturbance and locate a robot in the environments with certain occlusion. Design/methodology/approach At first, an iterative LIDAR-based pose tracking method is proposed. The LIDAR information is filtered and occupancy grid map is pre-processed. The sample generation and scoring are iterated so that the result is converged to the stable value. To improve the efficiency of sample processing, the integer-valued map indices of rotational samples are preserved and translated. All generated samples are analyzed to determine the maximum error direction. Then, a modified visual odometer is introduced for error compensation. The oriented fast and rotated brief (ORB) features are uniformly sampled in the image. A local map which contains key frames for reference is maintained. These two measures ensure that the modified visual odometer is able to return robust result which compensates the error of LIDAR-based pose tracking method in the maximum error direction. Findings Three experiments are conducted to prove the advantages of the proposed method. The proposed method can resist initial value disturbance with high computational efficiency, give back credible real-time result in the environment with abundant features and locate a robot in the environment with certain occlusion. Originality/value The proposed method is able to give back real-time pose tracking results with robustness. The iterative sample generation enables the robot to resist initial value disturbance. In each iteration, rotational and translational samples are separately generated to enhance computational efficiency. The maximum error direction of LIDAR-based pose tracking method is determined by principle component analysis and compensated by the result of modified visual odometer to give back correct pose in the environment with certain occlusion.

A Robust Control Method for Damping and Tracking of Secondary Network Voltage of a PV Based Hybrid AC/DC Microgrid

Future microgrids (MGs) will comprise of an enormous number of little secondary networks (SN) that will convey complex control for grid collaboration as well as tracking of operation. In this paper, a robust blended integral linear-quadratic-Gaussian (ILQG) controller is proposed for damping and tracking control of SN voltage of a PV based hybrid AC/DC MG against a number of operating conditions. The structure of this mixed controller is made by expanding the SN dynamics with the utilization of an integrator. The term SN signifies a network that can take power from the DC bus connected with the AC main grid and PV based DC grid. The aim of developing a DC bus is to provide the constant DC power in the SN load terminal which is made of a parallel combination of several uncertain and unknown loads that may produce the oscillation of its performance. The proposed blended ILQG controller provides a large control bandwidth that not only reduces the oscillation but also confirms the improved tracking performance over the number of loads in SN premises. Additionally, several uncertainties are considered within the SN dynamics to check the robustness of the proposed blended controller. The evaluation of this blended controller is studied for the various load dynamics and compared with the linear-quadratic Gaussian (LQG), linear-quadratic-regulator (LQR), and integral controller (IC) in terms of settling time, bandwidth and tracking performances.

A Robust Weld Seam Tracking Method by Using Intensity-Modulated Structured Light

The seam tracking method by using structured light is widely used in the automatic welding process. Most of the noise can be filtered out by a narrow-band optical filter whose center wavelength is the same as the structured light. However, it still suffers from light noise caused by sparks and fumes when the structured light is getting close to the welding pool. This limitation of the structured-light based method is inherent because light noise caused by the welding process has a wide spectrum and couldn't filter out by the optical filter. To circumvent this problem, we proposed a seam tracking method that can modulate the intensity of the structured light at a high frequency by an acoustic-optic modulator (AOM) and extract the modulated frequency from multiple seam images by Fourier transformation. As the frequency of noises such as sparks is random and different from the modulated frequency, the proposed method can remove these noises in the frequency domain directly. We demonstrated the robustness of this method by detecting seam in noisy welding conditions.

Robust RGB-D tracking via compact CNN features

Feature representation is at the core of visual tracking. This paper presents a robust tracking method in RGB-D videos. Firstly, the RGB and depth images are separately encoded using a hierarchical convolutional neural network (CNN) features. Secondly, in order to reduce computation cost, we exploit random projection to compress the CNN features. The high dimensional CNN features are randomly projected into a low dimensional feature space. The correlation filter tracking framework is then independently carried out in RGB and depth images. And backward tracking scheme is adopted to evaluate the tracking results in these two images. The final position is determined according to the tracked location in the two image channels. In addition, model updating is implemented adaptively. Our tracker is evaluated on two RGB-D benchmark datasets and achieves comparable results to the other state-of-the-art RGB-D tracking methods.

Robust Method for Mid-Activity Tracking and Evaluation of Ankle Health Post-Injury.

To present a robust methodology for evaluating ankle health during ambulation using a wearable device.Methods:We developed a novel data capture system that leverages changes within the ankle during ambulation for real-time tracking of bioimpedance. The novel analysis compares the range of reactance at 5kHz to the range of reactance at 100kHz; which removes the technique's previous reliance on a known baseline. To aid in interpretation of the measurements, we developed a quantitative simulation model based on a literature review of the effects on joint bioimpedance of variations in edematous fluid volume, muscle fiber tears, and blood flow changes.Results:The results of the simulation predicted a significant difference in the ratio of the range of the reactance from 5kHz to 100kHz between the healthy and injured ankles. These results were validated in 15 subjects - with 11 healthy ankles and 7 injured ankles measured. The injured subjects had lateral ankle sprains 2-4 weeks prior to the measurement. The analysis technique differentiated between the healthy and the injured population (p<<0.01), and a correlation (R = 0.8) with a static protocol previously validated for its sensitivity to edema.Conclusion:The technology presented can detect variations in ankle edema and structural integrity of ankles, and thus could provide valuable feedback to clinicians and patients during the rehabilitation of an ankle injury.Significance:This technology could lead to better-informed decision making regarding a patient's readiness to return to activity and / or tailoring rehabilitation activities to an individual's changing needs.