Accurate Tracking Results Research Articles

SiamTHN: Siamese Target Highlight Network for Visual Tracking

Siamese network based trackers develop rapidly in the field of visual object tracking in recent years. The majority of Siamese network based trackers now in use treat each channel in the feature maps generated by the backbone network equally, making the similarity response map sensitive to background influence and hence challenging to focus on the target region. Additionally, there are no structural links between the classification and regression branches in these trackers, and the two branches are optimized separately during training. Therefore, there is a misalignment between the classification and regression branches, which results in less accurate tracking results. In this paper, a Target Highlight Module is proposed to help the generated similarity response maps to be more focused on the target region. To reduce the misalignment and produce more precise tracking results, we propose a corrective loss to train the model. The two branches of the model are jointly tuned with the use of corrective loss to produce more reliable prediction results. Experiments on 5 challenging benchmark datasets reveal that the method outperforms current models in terms of performance, and runs at 38 fps, proving its effectiveness and efficiency.

Unified Transformer With Isomorphic Branches for Natural Language Tracking

Natural language tracking aims to localize the target object referred to by a language description using a sequence of bounding boxes in video frames. Compared with traditional visual single object tracking initialized with only a bounding box (BBox), this task introduces high-level semantic information to reduce the ambiguity of BBox and enhance the ability to retrieve the target in a global manner. Thus, it can yield more accurate and robust tracking results. Previous methods usually adopt off-the-shelf grounding and tracking branches to tackle this task, where feature representations are learned in isolation without benefiting each other. The two branches can associate with each other to discover crucial clues since the language description and the template image provide information from different sources. Therefore, we propose a unified transformer method for natural language tracking named TransNLT, which utilizes isomorphic Transformer structures for grounding and tracking branches where collaborative learning is enabled to construct comprehensive features for the target. In addition, we propose a Selective Feature Gathering (SFG) module which can integrate cross-modal global information of the tracking target from the visual template and language description. Through effective interaction of visual and language information, we can achieve better results than tracking with only a single modality. Extensive experiments on three popular natural language tracking benchmarks show our proposed TransNLT outperforms previous state-of-the-art methods.

Learning-based needle tip tracking in 2D ultrasound by fusing visual tracking and motion prediction.

Visual trackers are the most commonly adopted approach for needle tip tracking in ultrasound (US)-based procedures. However, they often perform unsatisfactorily in biological tissues due to the significant background noise and anatomical occlusion. This paper presents a learning-based needle tip tracking system, which consists of not only a visual tracking module, but also a motion prediction module. In the visual tracking module, two sets of masks are designed to improve the tracker's discriminability, and a template update submodule is used to keep up to date with the needle tip's current appearance. In the motion prediction module, a Transformer network-based prediction architecture estimates the target's current position according to its historical position data to tackle the problem of target's temporary disappearance. A data fusion module then integrates the results from the visual tracking and motion prediction modules to provide robust and accurate tracking results. Our proposed tracking system showed distinct improvement against other state-of-the-art trackers during the motorized needle insertion experiments in both gelatin phantom and biological tissue environments (e.g. 78% against <60% in terms of the tracking success rate in the most challenging scenario of "In-plane-static" during the tissue experiments). Its robustness was also verified in manual needle insertion experiments under varying needle velocities and directions, and occasional temporary needle tip disappearance, with its tracking success rate being >18% higher than the second best performing tracking system. The proposed tracking system, with its computational efficiency, tracking robustness, and tracking accuracy, will lead to safer targeting during existing clinical practice of US-guided needle operations and potentially be integrated in a tissue biopsy robotic system.

On Perpendicular Curve-Based Task Space Trajectory Tracking Control With Incomplete Orientation Constraint

The Incomplete Orientation Constraint (IOC), which does not require a controlled motion constrained by all three spatial directions, exists widely in a lot of robotic tasks. However, the IOC remains a challenge for existing methods, due to the nonlinear structure of the rotation group SO(3). Moreover, the IOCs are time varying in the trajectory tracking problem, which makes it more challenging than the set-point control. To address the IOC problems, we define, identify and prove the closed-form solution of the perpendicular curve in SO(3). Based on the proposed perpendicular curve, we develop a new trajectory tracking controller considering the IOC. Compared with existing methods, the proposed method can achieve faster and more accurate tracking results. Moreover, the proposed method can be applied not only to manipulators with redundancy (including both functional and intrinsic redundancy), but also to manipulators that are non-redundant. Furthermore, it is easier to incorporate a secondary optimization objective into consideration for the intrinsic redundant case, which is difficult for existing methods. The proposed method has been implemented on both simulations and experiments. The numerous simulation and experimental results validate the effectiveness and advantages of the proposed method. Note to Practitioners—The trajectory tracking control with IOC is important and can be applied in a wide spectrum of robotic-assisted manufacturing, e.g. arc-welding, engraving, etc.. In this paper, a perpendicular curve-based trajectory tracking method is proposed to consider the IOC automatically. It is no longer necessary to carefully plan a reachable orientation trajectory. Users only need to focus the planning of the tool direction, which is determined by the tasks. In addition, the proposed method can achieve faster and more accurate tracking result. Moreover, it is universal and can be applied to both redundant and non-redundant cases.

Improving maritime traffic surveillance in inland waterways using the robust fusion of AIS and visual data

To guarantee vessel traffic safety in inland waterways, the automatic identification system (AIS) and shore-based cameras have been widely adopted to monitor moving vessels. The AIS data could provide the unique maritime mobile service identity (MMSI), position coordinates (i.e., latitude and longitude), course over ground, and speed over ground for the vessels of interest. In contrast, the cameras could directly display the visual appearance of vessels but fail to accurately grasp the vessels’ identity information and motion parameters. In this paper, we propose to improve the maritime traffic surveillance in inland waterways using the robust fusion of AIS and visual data. It is able to obtain more accurate vessel tracking results and kinematic characteristics. In particular, to robustly track the visual vessels under complex scenarios, we first propose an anti-occlusion vessel tracking method based on the simple online and real-time tracking with a deep association metric (DeepSORT) method. We then preprocess and predict the vessel positions to obtain synchronous AIS and visual data. Before the implementation of AIS and visual data fusion, the AIS position coordinates in the geodetic coordinate system will be projected into the image coordinate system via the coordinate transformation. A multi-feature similarity measurement-based Hungarian algorithm is finally proposed to robustly and accurately fuse the AIS and visual data in the image coordinate system. For the sake of repeating fusion experiments, we have also presented a new multi-sensor dataset containing AIS data and shore-based camera imagery. The quantitative and qualitative experiments show that our fusion method is capable of improving the maritime traffic surveillance in inland waterways. It can overcome the vessel occlusion problem and fully utilizes the advantages of multi-source data to promote the maritime surveillance, resulting in enhanced vessel traffic safety and efficiency. In this work, the presented multi-sensor dataset and source code are available at https://github.com/QuJX/AIS-Visual-Fusion.

Feature-Based Object Detection and Tracking: A Systematic Literature Review

Correct object detection plays a key role in generating an accurate object tracking result. Feature-based methods have the capability of handling the critical process of extracting features of an object. This paper aims to investigate object tracking using feature-based methods in terms of (1) identifying and analyzing the existing methods; (2) reporting and scrutinizing the evaluation performance matrices and their implementation usage in measuring the effectiveness of object tracking and detection; (3) revealing and investigating the challenges that affect the accuracy performance of identified tracking methods; (4) measuring the effectiveness of identified methods in terms of revealing to what extent the challenges can impact the accuracy and precision performance based on the evaluation performance matrices reported; and (5) presenting the potential future directions for improvement. The review process of this research was conducted based on standard systematic literature review (SLR) guidelines by Kitchenam’s and Charters’. Initially, 157 prospective studies were identified. Through a rigorous study selection strategy, 32 relevant studies were selected to address the listed research questions. Thirty-two methods were identified and analyzed in terms of their aims, introduced improvements, and results achieved, along with presenting a new outlook on the classification of identified methods based on the feature-based method used in detection and tracking process.

High-resolution satellite video single object tracking based on thicksiam framework

ABSTRACT High-resolution satellite videos realize the short-dated gaze observation of the designated area on the ground, and its emergence has improved the temporal resolution of remote sensing data to the second level. Single object tracking (SOT) task in satellite video has attracted considerable attention. However, it faces challenges such as complex background, poor object feature representation, and lack of publicly available datasets. To cope with these challenges, a ThickSiam framework consisting of a Thickened Residual Block Siamese Network (TRBS-Net) for extracting robust semantic features to obtain the initial tracking results and a Remoulded Kalman Filter (RKF) module for simultaneously correcting the trajectory and size of the targets is designed in this work. The results of TRBS-Net and RKF modules are combined by an N-frame-convergence mechanism to achieve accurate tracking results. Ablation experiments are implemented on our annotated dataset to evaluate the performance of the proposed ThickSiam framework and other 19 state-of-the-art trackers. The comparison results show that our ThickSiam tracker obtains a precision value of 0.991 and a success value of 0.755 while running at 56.849 FPS implemented on one NVIDIA GTX1070Ti GPU.

Deep Correlation Filter Tracking With Shepherded Instance-Aware Proposals

Visual tracking is a core component of intelligent transportation systems and it is crucial to reduce or avoid traffic accidents. Recently, deep correlation filter (DCF) based trackers have exhibited good tracking performance. However, existing DCF based trackers are still ineffective to cope with large scale variations and severe distortions (e.g., heavy occlusions, significant deformations, large rotations, etc.), leading to the inferior performance. To address these issues, we develop a novel DeepCFIAP++ tracker, which incorporates effective shepherded instance-aware proposals into DCFs. DeepCFIAP++ can not only estimate the target scale at every frame but also re-detect the target in the case of severe distortions. Firstly, we propose to exploit both color and edge cues to generate complementary detection proposals to effectively handle various challenging scenarios. Then, we propose to utilize multi-layer target-specific deep features to rank the generated detection proposals and choose the instance-aware proposals, which will result in more robust tracking performance. Finally, we propose to use the DCFs to shepherd the instance-aware proposals toward their best locations, which will result in more accurate tracking results. Experimental results on five challenging datasets (i.e., OTB2013, OTB2015, VOT2016, VOT2017 and UAV20L) demonstrate that DeepCFIAP++ performs competitively with several other state-of-the-art DCF based trackers.

GARAT: Generative Adversarial Learning for Robust and Accurate Tracking

Object tracking by the Siamese network has gained its popularity for its outstanding performance and considerable potential. However, most of the existing Siamese architectures are faced with great difficulties when it comes to the scenes where the target is going through dramatic shape or environmental changes. In this work, we proposed a novel and concise generative adversarial learning method to solve the problem especially when the target is going under drastic changes of appearance, illumination variations and background clutters. We consider the above situations as distractors for tracking and joint a distractor generator into the traditional Siamese network. The component can simulate these distractors, and more robust tracking performance is achieved by eliminating the distractors from the input instance search image. Besides, we use the generalized intersection over union (GIoU) as our training loss. GIoU is a more strict metric for the bounding box regression compared to the traditional IoU, which can be used as training loss for more accurate tracking results. Experiments on five challenging benchmarks have shown favorable and state-of-the-art results against other trackers in different aspects.

A robust sperm cell tracking algorithm using uneven lighting image fixing and improved branch and bound algorithm

An accurate and robust sperm cells tracking algorithm that is able to detect and track sperm cells in videos with high accuracy and efficiency is presented. It is fast enough to process approximately 30 frames per second. It can find the correct path and measure motility parameters for each sperm. It can also adapt with different types of images coming from different cameras and bad recording conditions. Specifically, a new way is offered to optimize uneven lighting images to improve sperm cells detection which gives us the ability to get more accurate tracking results. The shape of each detected object is used to specify collided sperms and utilized dynamic gates which become bigger and smaller according to the sperm cell's speed. For assigning tracks to the detected sperm cells positions an improved version of branch and bound algorithm which is faster than the normal one is offered. This sperm cells tracking algorithm outperforms many of the previous algorithms as it has lower error rate in both sperm detection and tracking. It is compared with six other algorithms, and it gives lower tracking error rates. This method will allow doctors and researchers to obtain sperm motility data instantly and accurately.

An Adaptive Sensor Fusion Framework for Pedestrian Indoor Navigation in Dynamic Environments

Indoor navigation is a representative application of an indoor positioning system that uses a variety of equipment, including smartphones with various sensors. Many indoor navigation systems utilize Wi-Fi signals, as well as a variety of inertial sensors, such as a 3D accelerometer, digital compass, gyroscope, and barometer, to improve the accuracy of user location tracking. The inertial sensors are vulnerable to changes in the surrounding environments and sensitive to users behavior, but little research has been conducted on sensor fusion under these conditions. In this paper, we propose a dynamic sensor fusion framework (DSFF) that provides accurate user tracking results by dynamically calibrating inertial sensor readings in a sensor fusion process. The proposed method continually learns the errors and biases of each sensor due to the changes in user behavior patterns and surrounding environments. The learned patterns are then dynamically applied to the user tracking process to yield accurate results. The results of experiments conducted in both a single-story and a multi-story building confirm that DSFF provides accurate tracking results. The scalability of the DSFF will enable it to provide more accurate tracking results with various sensors, both existing and under development.

Real-Time Multi-Object Magnetic Tracking for Multi-Arm Continuum Robots

Multi-arm wire-driven continuum robots can provide dexterous maneuvers during minimally invasive surgeries (MISs). To ensure safety during MIS, it is critical to obtain feedback information such as the poses of end effectors on all arms. However, precise and real-time pose information of multi-object cannot be obtained within the human body due to the high computational cost in the current optimization approach of magnetic tracking systems. In this article, we propose to track the poses of multiple end effectors of a multi-arm continuum robot by permanent magnetic tracking technology. A permanent magnet is mounted at each distal end of the flexible arms and a sensor array is used to sample the magnetic field signals. The extended Kalman filter (EKF) algorithm is utilized to fuse the poses estimated from the kinematic model and the magnetic field model to realize multi-target positioning for surgical navigation. The proposed method is validated by experiments. The mean position error is 1.32 mm and the computing time is 1.5 ms. As a result, the proposed method can effectively provide accurate tracking results and meanwhile decrease the tracking latency for real-time applications.

Active Disturbance Rejection Control for Reference Trajectory Tracking Tasks in the Pendubot System

In this article, an Extended State Observer (ESO) based Active Disturbance Rejection Control (ADRC) scheme is applied to the Pendubot system for a trajectory tracking tasks. The tangent linearization of the system allows to implement the control scheme taking advantage of the differential flatness property, while including a cascade configuration. The proposed method assumes a limited knowledge of the underactuated system where the control input gain and the order of the system are the only needed data. The scheme is experimentally tested leading to accurate tracking results.

Fusion of the SLAM with Wi-Fi-Based Positioning Methods for Mobile Robot-Based Learning Data Collection, Localization, and Tracking in Indoor Spaces.

The ability to estimate the current locations of mobile robots that move in a limited workspace and perform tasks is fundamental in robotic services. However, even if the robot is given a map of the workspace, it is not easy to quickly and accurately determine its own location by relying only on dead reckoning. In this paper, a new signal fluctuation matrix and a tracking algorithm that combines the extended Viterbi algorithm and odometer information are proposed to improve the accuracy of robot location tracking. In addition, to collect high-quality learning data, we introduce a fusion method called simultaneous localization and mapping and Wi-Fi fingerprinting techniques. The results of the experiments conducted in an office environment confirm that the proposed methods provide accurate and efficient tracking results. We hope that the proposed methods will also be applied to different fields, such as the Internet of Things, to support real-life activities.

Zero‐variance minibatch Monte Carlo for pixel‐wise visual tracking

In this study, the authors present a novel visual tracking method using the pixel-wise posterior estimation and minibatch Monte Carlo sampling. To avoid background pixels in the noisy bounding box representation, they estimate the posteriors in a pixel-wise manner. To boost the pixel-wise posterior estimation, they adopt minibatch Monte Carlo sampling, where only a small portion of pixels are used for inference. Experimental results demonstrate that the proposed visual tracker produces accurate tracking results using a small portion of pixels for the posterior estimation and is comparable to state-of-the-art methods.

An INS and UWB Fusion-Based Gyroscope Drift Correction Approach for Indoor Pedestrian Tracking.

Information fusion combining inertial navigation and radio frequency (RF) technologies, is commonly applied in indoor positioning systems (IPSs) to obtain more accurate tracking results. The performance of the inertial navigation system (INS) subsystem is affected by sensor drift over time and the RF-based subsystem aims to correct the position estimate using a fusion filter. However, the inherent sensor drift is usually not corrected during fusion, which leads to increasingly erroneous estimates over a short period of time. Among the inertial sensor drifts, gyroscope drift has the most significant impact in determining the correct orientation and accurate tracking. A gyroscope drift correction approach is proposed in this study and is incorporated in an INS and ultra-wideband (UWB) fusion IPS where only distance measurements from UWB subsystem are used. The drift correction approach is based on turn detection to account for the fact that gyroscope drift is accumulated during a turn. Practical pedestrian tracking experiments are conducted to demonstrate the accuracy of the drift correction approach. With the gyroscope drift corrected, the fusion IPS is able to provide more accurate tracking performance and achieve up to 64.52% mean position error reduction when compared to the INS only tracking result.

Recurrent Neural Network-Based Hybrid Localization for Worker Tracking in an Offshore Environment

Accidents involving marine crew members and passengers are still an issue that must be studied and obviated. Preventing such accidents at sea can improve the quality of life on board by ensuring a safe ship environment. This paper proposes a hybrid indoor positioning method, an approach which is becoming common on land, to enhance maritime safety. Specifically, a recurrent neural network (RNN)-based hybrid localization system (RHLS) that provides accurate and efficient user-tracking results is proposed. RHLS performs hybrid positioning by receiving wireless signals, such as Wi-Fi and Bluetooth, as well as inertial measurement unit data from smartphones. It utilizes the RNN to solve the problem of tracking accuracy reduction that may occur when using data collected from various sensors at various times. The results of experiments conducted in an offshore environment confirm that RHLS provides accurate and efficient tracking results. The scalability of RHLS provides managers with more intuitive monitoring of assets and crews, and, by providing information such as the location of safety equipment to the crew, it promotes welfare and safety.

A novel variable structure multi-model approach based on error-ambiguity decomposition

Model Set Adaptation (MSA) plays a key role in the Variable Structure Multi-Model tracking approach (VSMM). In this paper, the Error-Ambiguity Decomposition (EAD) principle is adopted to derive the EAD-MSA criterion that is optimal in the sense of minimizing the square error between the estimate and the truth. Consequently, the EAD Variable Structure first-order General Pseudo Bayesian (EAD-VSGPB1) algorithm and the EAD Variable Structure Interacting Multiple Model (EAD-VSIMM) algorithm are constructed. The proposed algorithms are tested in two groups of maneuvering target tracking scenarios under different modes and observation error conditions. The simulation results demonstrate the effectiveness of the EAD-VSMM approach and show that, compared to some existing multi-model algorithms, the proposed EAD-VSMM algorithms achieve more robust and accurate tracking results.

Flat Filtering Cascade Control of Fourth Order Systems

Abstract In this article, the analysis and implementation of an alternative Flat Filtering Control for a class of partially known fourth order flat systems is given. The Flat filtering control uses the cascade property of the system, which leads to a simplified control design in which high order time derivatives are algebraically simplified in terms of low order measurable states and the subsequent integral compensation. The control proposal is implemented and validated experimentally in a fourth order mechanical system (rotatory flexible joint) with accurate tracking results.

Siamese Cascaded Region Proposal Networks With Channel-Interconnection-Spatial Attention for Visual Tracking

Trackers based on Siamese networks show great potential in tracking accuracy and speed. However, it is still challenging to adapt offline training model to online tracking. In this paper, a Siamese based tracker (SCRPN-CISA) is proposed, which integrates three attention mechanisms and a novel Cascaded Region Proposal Networks (RPN) architecture, for improving the feature extraction ability, adaptability and discrimination ability in complex scenes. Firstly, the deep network VGG-Net-D is adopted as the backbone network in the Siamese framework to increase the feature extraction capability. Then, a Channel-Interconnection-Spatial Attention module is constructed to enhance the adaptive and discriminative capability of the model. Next, a Deconvolution Adjust Block is built to fusion cross-layer features. Finally, a Three-Layer Cascaded RPN is conceived to acquire the foreground-background classification and bounding box regression by correlation calculation, and moreover, a proposal region screening strategy is presented to obtain more accurate tracking results. Experiments on OTB-2015, UAV123, VOT2016, and VOT2019 benchmarks demonstrate that, the proposed tracker (SCRPN-CISA) achieves competitive performance compared with the state-of-the-art trackers.