Tracking Results Research Articles

Isolation of Macrolactin A from a new Bacillus amyloliquefaciens and its aphicidal activity against Rhopalosiphum padi.

Rhopalosiphum padi is a major infection affecting cereal crops in the boundary area. However, continuous use of chemical pesticides has increased cases of drug resistance in its field population. Therefore, we aimed to verify the insecticidal properties of Bacillus amyloliquefaciens YJNbs21.10 against aphids, isolated to determine the bioactivity of its metabolite Macrolactin A against aphids for the first time. The results of activity tracking showed that the fermentation broth of YJNbs21.10 had the best inhibitory efficacy against R. padi, and the corrected efficiency reached 95.57% after 24 h. With the continuous separation and test, the efficiency of the active components decreased: Macrolactin A, as the most active substance, had a control activity against aphids under 500 mg L-1 of 74.64% at 72 h, (which was significantly lower than that of fermentation broth, indicating a synergistic effect between the active substances of each part of the strain. In addition, the result of the stereomicroscope showed that Macrolactin A damaged the body wall of aphids. The toxicity of Macrolactin A to R. padi was confirmed through the gradient test. In this study, Bacillus amyloliquefaciens YJNbs21.10 exhibited comparable inhibitory ability to chemical pesticides suggesting its potential to provide effective biological control on aphids. The biological activity of Macrolactin A against aphids was also verified for the first time, in this experiment, the EC50 of this substance against aphids was 169.02 mg L-1 (24 h), which provided strong evidence that YJNbs21.10 may act as an effective agent for the prevention of aphid. © 2024 Society of Chemical Industry.

Research on High-Dynamic Tracking Algorithms for FH-BOC Signals

The rapid development of Low Earth Orbit (LEO) satellite navigation systems requires modulation schemes with strong anti-jamming capabilities, high spectral efficiency, and the ability to achieve precise tracking in high-dynamic environments. Traditional Binary Offset Carrier (BOC) modulation suffers from multi-peak ambiguity, leading to false lock issues. To address this, FH-BOC modulation, which integrates BOC modulationand frequency hopping, significantly improves both anti-jamming performance and spectral efficiency. Against this background, this paper proposes a comprehensive high-dynamic tracking algorithm for FH-BOC signals. (1) Based on the adaptive Kalman filter algorithm, high-precision carrier tracking was achieved in high-dynamic environments. (2) By leveraging the correlation between the ranging code and frequency-hopping offset carrier, a composite pseudo-code is generated through the XOR operation, and a corresponding composite code-tracking loop is introduced. (3) Based on code loop tracking results, the frequency-hopping moments of the subcarrier are detected, and a phase-locked loop for the frequency-hopping subcarrier is established. Simulation results indicate that the algorithm achieves centimeter-level pseudorange measurement accuracy for FH-BOC navigation signals under the JPL high-dynamic model.

Multi-Rotor Drone-Based Thermal Target Tracking with Track Segment Association for Search and Rescue Missions

Multi-rotor drones have expanded their range of applications, one of which being search and rescue (SAR) missions using infrared thermal imaging. This paper addresses thermal target tracking with track segment association (TSA) for SAR missions. Three types of associations including TSA are developed with an interacting multiple model (IMM) approach. During multiple-target tracking, tracks are initialized, maintained, and terminated. There are three different associations in track maintenance: measurement–track association, track–track association for tracks that exist at the same time (track association and fusion), and track–track association for tracks that exist at separate times (TSA). Measurement–track association selects the statistically nearest measurement and updates the track with the measurement through the IMM filter. Track association and fusion fuses redundant tracks for the same target that are spatially separated. TSA connects tracks that have become broken and separated over time. This process is accomplished through the selection of candidate track pairs, backward IMM filtering, association testing, and an assignment rule. In the experiments, a drone was equipped with an infrared thermal imaging camera, and two thermal videos were captured of three people in a non-visible environment. These three hikers were located close together and occluded by each other or other obstacles in the mountains. The drone was allowed to move arbitrarily. The tracking results were evaluated by the average total track life, average mean track life, and average track purity. The track segment association improved the average mean track life of each video by 99.8% and 250%, respectively

Intelligent vehicle path tracking coordinated optimization based on dual-steering cooperative game with fault-tolerant function

Since that a single controller for intelligent vehicle path tracking is difficult to adapt to complex working conditions and achieve good system control performance, inspired by the concept of control redundancy, an adaptive weight based dual-steering control cooperative game coordinated optimization with fault-tolerant control method for intelligent vehicle path tracking is proposed in this paper. Firstly, the three-degree-of-freedom of lateral, yaw and longitudinal directions is considered to establish the vehicle single-track dynamics model, and two model predictive path tracking steering controllers with different control objective emphases in tracking accuracy and yaw stability are designed. Then, the cooperative game theory is introduced to model the steering interaction of dual controllers, and its Pareto equilibrium optimal solution is solved by the distributed model predictive control framework. According to the influence trend of two game players’ relative participation weights on the path tracking results, an adaptive weight adjustment strategy based on predicted output deviation is designed, to achieve the coordinated optimization of dual steering control, and the steering fault-tolerant control is also realized by cooperative game in some cases of steering partial failure. Carsim/Simulink co-simulation and hardware-in-loop test under the variable curvature path and control failure conditions prove that by the proposed method intelligent vehicle path tracking dual steering control can be coordinated optimized and optimal path tracking performance is obtained, and demonstrate the effectiveness of the adaptive weight adjustment method which has better fault-tolerant control compared to fixed weight allocation under different degrees of steering output failures.

Depth gate tracking method based on historical sounding results in MBES

Gate tracking technology plays a crucial role in the autonomous detection of multi-beam echo sounding. The quality of the tracking algorithm directly impacts the quality of the estimate data. Here, we propose a new method for depth gate tracking in multi-beam echo sounding based on the assumption that historical sounding results are geometrically similar to the current ping results. Considering Hausdorff distance measures how far two subsets of a metric space are from each other. An evaluation method based on the concept of similarity distance that relies on the Modified Hausdorff distance is proposed here to measure the similarity between the detection results of different pings and determine the maximum number of historical pings that can be tracked. Then, this model is combined with the iterative closest point registration algorithm to align the bathymetric results of historical pings to the current ping, eliminating the need for prior registration. The registered data are used to initialize the particle distribution in the tracking particle filter and a distance-weighted importance function is established for each beam. Validation on measured data has shown that the proposed method is effective in tracking seabed topography and providing stable gate tracking results.

SlicerCineTrack: An open-source research toolkit for target tracking verification in 3D Slicer

Purpose:Target motion monitoring plays a significant role in several computer-assisted interventions. However, ensuring the reliability of tracking algorithms can be challenging without adequate tools. We introduce SlicerCineTrack, a free open-source research toolkit, designed to provide users with a user-friendly interface for visualizing their target tracking results. Methods:SlicerCineTrack was developed as an extension to 3D Slicer. It enables users to visualize target tracking results by sequentially playing back cine medical images, and simultaneously overlaying the target segmentation at the locations indicated by the tracking results. The extension was evaluated by established experts in computer-assisted interventions and image-guided therapy. Results:SlicerCineTrack is available for download from the 3D Slicer extension catalog for stable releases, and its GitHub repository for preview releases. Evaluation results demonstrate SlicerCineTrack’s effectiveness in discriminating between different tracking performances. Moreover, the experts found the extension convenient to use due to its intuitive and user-friendly interface. Conclusions:SlicerCineTrack was found to be effective at verifying the reliability of tracking algorithms. In turn, SlicerCineTrack shows potential for target tracking verification, as well as algorithm validation and refining through parameter tuning.

Seismic horizon tracking based on TransUnet model

Seismic horizons are of paramount importance for building structure models and stratigraphic interpretations. However, currently, horizon tracking is generally obtained through manual tracking or a combination of traditional automatic tracking and manual tracking, which is a highly time-consuming and error-prone process. Despite the many methods proposed for automated horizon tracking, tracking horizons with complex features still face numerous challenges.This paper proposes a method for horizon tracking using the TransUnet model, which can be applied to track both individual horizons and multiple horizons simultaneously. This method is implemented through deep learning semantic segmentation. Training is conducted using a deep learning encoder-decoder structure to establish the mapping relationship between seismic data and horizon data. The TransUnet model builds upon the Unet model by incorporating Transformer modules, enabling the model to possess both local and global attention capabilities. The ultimate results of horizon tracking reveal that the TransUnet model is more proficient at tracking horizons in areas with complex seismic reflections compared to the Unet model. In single and multiple horizon tracking, the root mean square error (RMSE) and coefficient of determination (R2 ) calculated from horizons predicted by the TransUNet model and manually tracked horizons are significantly lower than those of the U-Net model, this research demonstrates that the proposed method is more efficient and accurate in tracking the horizons of three-dimensional seismic volumes compared to traditional approaches.

CF-SOLT: Real-time and accurate traffic accident detection using correlation filter-based tracking

Traffic accident detection using video surveillance is valuable research work in intelligent transportation systems. It is useful for responding to traffic accidents promptly that can avoid traffic jam or prevent secondary accident. In traffic accident detection, tracking occluded vehicles in real-time and accurately is one of the major sticking points for practical applications. In order to improve the tracking of occluded vehicles for traffic accident detection, this paper proposes a simple online tracking scheme with correlation filters (CF-SOLT). The CF-SOLT method utilizes a correlation filter-based auxiliary tracker to assist the main tracker. This auxiliary tracker helps prevent target ID switching caused by occlusion, enabling accurate vehicle tracking in occluded scenes. Based on the tracking results, a precise traffic accident detection algorithm is developed by integrating behavior analysis of both vehicles and pedestrians. The improved accident detection algorithm with the correlation filter-based auxiliary tracker can provide shorter response time, enabling quick identification and detection of traffic accidents. The experiments are conducted on the VisDrone2019, MOT-Traffic and Dataset of accident to evaluate the performances metrics of MOTA, IDF1, FPS, precision, response time and others. The results show that CF-SOLT improves MOTA and IDF1 by 5.3% and 6.7%, accident detection precision by 25%, and reduces response time by 56 s.

Efficient transformer tracking with adaptive attention

AbstractRecently, several trackers utilising Transformer architecture have shown significant performance improvement. However, the high computational cost of multi‐head attention, a core component in the Transformer, has limited real‐time running speed, which is crucial for tracking tasks. Additionally, the global mechanism of multi‐head attention makes it susceptible to distractors with similar semantic information to the target. To address these issues, the authors propose a novel adaptive attention that enhances features through the spatial sparse attention mechanism with less than 1/4 of the computational complexity of multi‐head attention. Our adaptive attention sets a perception range around each element in the feature map based on the target scale in the previous tracking result and adaptively searches for the information of interest. This allows the module to focus on the target region rather than background distractors. Based on adaptive attention, the authors build an efficient transformer tracking framework. It can perform deep interaction between search and template features to activate target information and aggregate multi‐level interaction features to enhance the representation ability. The evaluation results on seven benchmarks show that the authors’ tracker achieves outstanding performance with a speed of 43 fps and significant advantages in hard circumstances.

The Effect of Low-Dose CT Protocols on Shoulder Model-Based Tracking accuracy Using Biplane Videoradiography.

Model-based tracking is being increasingly used to quantify shoulder kinematics and typically employs computed tomography (CT) to create the 3D bone volumes, which adds to the total radiation exposure. Lower-dose CT protocols may be possible given the contrast between bone and the surrounding soft tissues. The purpose of this study was to describe the dose-accuracy tradeoff between low-dose CT scans and the kinematic tracking accuracy of the humerus, scapula, and clavicle when tracked using an intensity-based registration algorithm. Three fresh-frozen cadavers consisting of the torso and bilateral shoulders were tested. The CT protocols investigated included a full-dose protocol and 4 experimental low-dose protocols that modulated x-raytube current and peak voltage. Bead-based tracking (i.e., radiostereometric analysis) served as the reference standard to which model-based tracking results were compared. Accuracy was described in terms of both segmental (humerus, scapula, and clavicle) and joint (glenohumeral, acromioclavicular) kinematics using root-mean-square (RMSE), bias, precision, and worst-case errors. The low-dose CT scans resulted in an average dose reduction of 70.6-92.8%. RMSEs tended to increase as CT dose decreased with average glenohumeral errors increasing from 0.5° and 0.6 mm to 0.6° and 0.6 mm between the highest and lowest-dose protocols, and average acromioclavicular errors increasing from 0.6° and 0.8 mm to 0.7° and 0.9 mm. However, the difference in joint kinematic errors between the highest and lowest-dose CT scanning protocols was generally small (≤0.3°, ≤ 0.1 mm). It is possible to substantially reduce the CT dose associated with shoulder motion analysis using biplane videoradiography without significantly impacting data fidelity.

Truncated predictive control for delayed cyber–physical systems under deception attacks

In this work, the state tracking, input delay prediction, uncertainty and disturbance rejection problems are studied for the delayed cyber–physical systems under uncertainties, nonlinearities and external disturbances in which the addressed model is described by the fuzzy technique. More specifically, such an input-delayed system is linearized through an interval type-2 fuzzy technique accompanied by a parallel distribution compensation method. Further, the presence of constant input delays in the examined system is proficiently compensated with the assistance of a truncated predictor feedback approach. In parallel, the impacts of external disturbances and uncertainties that are occurred in the system are estimated by means of uncertainty and disturbance estimator method in an efficacious way. With the help of this estimated information, an uncertainty and disturbance estimator-based truncated predictive tracking control protocol is formulated to acquire the state tracking results of the underlying system in the presence of external disturbances and uncertainties. Remarkably, in this study, the interval type-2 fuzzy is blended with the designed reference system. Furthermore, the secure tracking control law is designed for the considered system under the random deception attacks. To address the deception attacks, the Bernoulli stochastic distribution is considered in this study. Moreover, the constraints are expressed for computing the truncated predictive feedback control gain matrices. Conclusively, the numerical simulation analysis is carried out to demonstrate the usefulness of the devised approach.

First test beam of the DMAPS-based proton tracker at the pMBRT facility at the Curie Institute

Objective.Proton radiotherapy's efficacy relies on an accurate relative stopping power (RSP) map of the patient to optimise the treatment plan and minimize uncertainties. Currently, a conversion of a Hounsfield Units map obtained by a common x-ray computed tomography (CT) is used to compute the RSP. This conversion is one of the main limiting factors for proton radiotherapy. To bypass this conversion a direct RSP map could be obtained by performing a proton CT (pCT). The focal point of this article is to present a proof of concept of the potential of fast pixel technologies for proton tracking at clinical facilities.Approach.A two-layer tracker based on the TJ-Monopix1, a depleted monolithic active pixel sensor (DMAPS) chip initially designed for the ATLAS, was tested at the proton minibeam radiotherapy beamline at the Curie Institute. The chips were subjected to 100 MeV protons passing through the single slit collimator (0.4×20mm2) with fluxes up to1.3×107p s-1 cm-2. The performance of the proton tracker was evaluated with GEANT4 simulations.Main results.The beam profile and dispersion in air were characterized by an opening of 0.031 mm cm-1, and aσx=0.172mm at the position of the slit. The results of the proton tracking show how the TJ-Monopix1 chip can effectively track protons in a clinical environment, achieving a tracking purity close to 70%, and a position resolution below 0.5 mm; confirming the chip's ability to handle high proton fluxes with a competitive performance.Significance.This work suggests that DMAPS technologies can be a cost-effective alternative for proton imaging. Additionally, the study identifies areas where further optimization of chip design is required to fully leverage these technologies for clinical ion imaging applications.

Robust tracking and detection based on radar camera fusion filtering in urban autonomous driving

The primary goal of autonomous vehicles is vehicle safety, achieved through vehicle planning and control based on the understanding of the driving environment. To understand the surrounding environment, most autonomous driving systems utilize lidar and cameras for tracking nearby objects. However, both sensors are effective only within a short range of 50 m and lack robustness in adverse conditions, thereby failing to ensure the stability of object tracking and state estimation. Therefore, to enable continuous tracking over long distances, this paper proposes a robust tracking and detection system based on radar-camera fusion in urban autonomous driving. In this system, radar is used as the primary sensor to achieve uninterrupted tracking over longer distances, and cameras are employed to compensate for the relatively inaccurate measurements of the radar, thereby enhancing tracking accuracy. To achieve robust tracking results in urban environments and from surrounding objects, the track management process utilizes velocity values, unique to radar compared to other sensors, to establish associations between tracklets and measurements. Subsequently, the process selects valid tracklets among those unmatched by considering the relationship between the time of initialization for each tracklet and the current time. To enhance the accuracy of tracking results, the state update process integrates radar and camera information using a decentralized Kalman filter. This filter structure not only improves accuracy but also ensures robustness against sensor failure and real-time performance in the fusion process. The algorithm is implemented in autonomous vehicles equipped with radar and low-cost cameras and is validated on test tracks, as well as in urban and highway environments. The experimental results confirm that the algorithm continuously tracks targets over long distances and demonstrates enhanced tracking results through fusion with the camera, conducting the process efficiently and robustly.

Visual tracking algorithm based on template updating and dual feature enhancement

Aiming at the problem of tracking failure due to target deformation, flipping and occlusion in visual tracking, a template updating algorithm based on image structural similarity is proposed by dynamically updating the template to adapt to the changes of the target during tracking, specifically, a queue is used to save the recent N -frame tracking results, and a decision is made on whether to update the template or not based on the structural similarity score between the current tracking results and the template image, and if updated, the template is matched from the historical N -frame tracking results as the new template. If updated, the optimal target image is matched from the historical N -frame tracking results as a new template for subsequent tracking. The tracking feature enhancement module and segmentation feature enhancement module are also designed based on SiamMask network. The tracking feature enhancement module consists of non-local operations and convolutional downsampling, which is used to establish contextual correlation, enhance the target features, suppress the background interference, improve the tracking robustness, and solve the feature attenuation problem due to the occlusion of the target. The segmentation feature enhancement module introduces the convolutional block attention module and deformable convolution to improve the network's ability to capture channel and spatial features, adaptively learn the shape and contour information of the target, and enhance the network's segmentation accuracy of the tracked target. , which in turn improves the tracking accuracy. Experiments show that the proposed algorithm performs well and stably in solving the above problems, improving the expected average overlap rate by 5.2%, 5.3%, and 2.5%, and the robustness by 6%, 7.9%, and 15.6%on the VOT2016, VOT2018, and VOT2019 datasets , respectively, and achieving a real-time speed of 91 frames per second, when compared to the baseline SiamMask.

EKF-based parameter estimation method for radar maneuvering target with unknown time information

Moving target detection (MTD) is a research hotspot in radar signal processing. Generally, the time information of non-cooperative moving targets entering and leaving a radar coverage area is unknown, which would lead to severe performance loss for target parameter estimation, detection, and imaging. Unlike our previous research work, this paper addresses the motion parameters estimation and refocusing problem for a radar maneuvering target with unknown entry and departure time. A computationally efficient method that utilizes extended Kalman filtering (EKF) for phase tracking is proposed to estimate the entry and departure times. The proposed method first performs range cell migration correction (RCMC) on the pulse compression echo signal. Then, the maneuvering target signal is modeled as a polynomial phase signal (PPS) and utilizes the EKF to construct a binary state-space equation for polynomial phase tracking. Finally, by comparing the phase tracking results of the noise cell and the signal cell, one can derive estimates for the entry/departure time and motion parameters. Compared with existing methods, the proposed method avoids multi-dimension searching on the parameter space, so it has a prominent advantage in computational complexity. Moreover, the core of the proposed method lies in tracking the polynomial phase, which is not constrained by the order of target motion, and has wider applicability in practice. Both simulated and public radar data are used to validate the effectiveness of the proposed method.

Campylobacteriosis Outbreak Linked to Municipal Water, Nebraska, USA, 20211.

In September 2021, eight campylobacteriosis cases were identified in a town in Nebraska, USA. We assessed potential exposures for a case-control analysis. We conducted whole-genome sequencing on Campylobacter isolates from patients' stool specimens. We collected large-volume dead-end ultrafiltration water samples for Campylobacter and microbial source tracking testing at the Centers for Disease Control and Prevention. We identified 64 cases in 2 waves of illnesses. Untreated municipal tap water consumption was strongly associated with illness (wave 1 odds ratio 15.36; wave 2 odds ratio 16.11). Whole-genome sequencing of 12 isolates identified 2 distinct Campylobacter jejuni subtypes (1 subtype/wave). The town began water chlorination, after which water testing detected coliforms. One dead-end ultrafiltration sample yielded nonculturable Campylobacter and avian-specific fecal rRNA genomic material. Our investigation implicated contaminated, untreated, municipal water as the source. Results of microbial source tracking supported mitigation with continued water chlorination. No further campylobacteriosis cases attributable to water were reported.

Rotifer detection and tracking framework using deep learning for automatic culture systems

Although rotifers (Brachionus plicatilis sp. complex) are an important first feed source in marine fish aquaculture, their management is quite time-consuming because their populations and movements need to be monitored daily. This management is still performed manually, and automation is required. If we could make good use of the recent breakthroughs in deep learning, the automating a rotifer culture system could be realized. We propose a deep learning framework for detecting and tracking rotifers as a basis for such automation and carefully verify its accuracy. Experimental results showed that a mean average precision of 88.5 % was achieved for detection, and a higher-order tracking accuracy of 88.7 % was achieved for tracking, indicating the suitability of deep learning methods for predicting the state of rotifers. In addition, this research will contribute to the development of the field by releasing the trained model and code for visualizing the tracking results, as well as an annotated dataset with over 30,000 instances.

Real-Time Identification and Nonlinear Control of a Permanent-Magnet Synchronous Motor Based on a Physics-Informed Neural Network and Exact Feedback Linearization

This work proposes a novel method for the real-time identification and nonlinear control of a permanent-magnet synchronous motor (PMSM) based on a Physics-Informed Neural Network (PINN) and the exact feedback linearization approach. The proposed approach is presented in a direct-quadrature framework, where the quadrature current and the rotational speed are selected as outputs and the direct and quadrature voltages are selected as inputs. A nonlinear difference equation is selected to describe the physical dynamics of the PMSM, and a PINN is designed based on the aforementioned structure. A simplified training scheme is designed for the PINN based on a least-squares structure to facilitate online training in real time. A nonlinear controller based on exact feedback linearization is designed by considering the nonlinear model of the system identified based on the PINN. Therefore, the proposed approach involves identification and control in real time, where the PINN is trained online. In order to track the reference for the rotational speed, a nonlinear controller with integral action based on exact feedback linearization is designed based on a linear quadratic regulator. As a result, the proposed approach can be used to identify the system to be controlled in real time, and it is able to track any small change in the real model; in addition, it is robust to both external and internal disturbances, such as variations in torque load and resistance. The proposed approach is evaluated through simulation and using a real PMSM, and the results of reference tracking are evaluated under disturbances. The identification performance is evaluated by using a Taylor diagram under closed-loop and open-loop structures, where ARX and NARX structures are used for comparison. It is thereby verified that this novel proposed control approach involving a PINN-based model can adequately track the dynamics of a PMSM system, where the performance of the proposed nonlinear control is maintained even when using the identified model based on the PINN.

ENHANCED MODEL-FREE PREDICTIVE CONTROL FOR VOLTAGE SOURCE INVERTERS USING AN ADAPTIVE OBSERVER

In this paper, a free model predictive control based on the active vector execution time (AVET-MFPC) using an adaptive observer is proposed for two-level voltage source inverters. The traditional model-free predictive control (MFPC) uses the sampling period to select one voltage vector for all candidate vectors according to the minimizing cost function principle. With the proposed control, two vectors are selected at one sampling period. The first vector is an active vector that uses the execution time of the active vector to select it, while the second one is a zero vector as it is applied after the active vector. The execution time is calculated using the ultra-local model (ULM) equation. In the traditional MFPC, the factor in the ULM is chosen with approximate values ranging between ±50 % of the nominal value. This paper proposes an adaptive sliding mode observer (ASMO) with an improved design to observe the variation of this factor, especially in case of mismatch parameters and during a step change in the reference signal. Combining the proposed ASMO observer and the AVET-MFPC controller gives faster system response, good tracking results, and less computational burden. Finally, the effectiveness of the proposed control model is approved and confirmed under various conditions, as well as the simulations carried out and the results obtained.

Exploring Point-BEV Fusion for 3D Point Cloud Object Tracking With Transformer.

With the prevalent use of LiDAR sensors in autonomous driving, 3D point cloud object tracking has received increasing attention. In a point cloud sequence, 3D object tracking aims to predict the location and orientation of an object in consecutive frames. Motivated by the success of transformers, we propose Point Tracking TRansformer (PTTR), which efficiently predicts high-quality 3D tracking results in a coarse-to-fine manner with the help of transformer operations. PTTR consists of three novel designs. 1) Instead of random sampling, we design Relation-Aware Sampling to preserve relevant points to the given template during subsampling. 2) We propose a Point Relation Transformer for effective feature aggregation and feature matching between the template and search region. 3) Based on the coarse tracking results, we employ a novel Prediction Refinement Module to obtain the final refined prediction through local feature pooling. In addition, motivated by the favorable properties of the Bird's-Eye View (BEV) of point clouds in capturing object motion, we further design a more advanced framework named PTTR++, which incorporates both the point-wise view and BEV representation to exploit their complementary effect in generating high-quality tracking results. PTTR++ substantially boosts the tracking performance on top of PTTR with low computational overhead. Extensive experiments over multiple datasets show that our proposed approaches achieve superior 3D tracking accuracy and efficiency.