Video-based vehicle tracking considering occlusion

To track the vehicles under occlusion, a vehicle tracking algorithm based on blocks is proposed. The target vehicle is divided into several blocks of uniform size, in which the edge block can overlap its neighboring blocks. All the blocks＇ motion vectors are estimated, and the noise motion vectors are detected and adjusted to decrease the error of motion vector estimation. Then, by moving the blocks based on the adjusted motion vectors, the vehicle is tracked. Aiming at the occlusion between vehicles, a Markov random field is established to describe the relationship between the blocks in the blocked regions. The neighborhood of blocks is defined using the Euclidean distance. An energy function is defined based on the blocks＇ histograms and optimized by the simulated annealing algorithm to segment the occlusion region. Experimental results demonstrate that the proposed algorithm can track vehicles under occlusion accurately.

Real-time vehicle tracking for traffic monitoring systems

A real-time vehicle tracking method is proposed for trattlC monitoring system at roau mte^cc- tions, and the vehicle tracking module consists of an initialization stage and a tracking stage. Li- cense plate location based on edge density and color analysis is used to detect the license plate re- gion for tracking initialization. In the tracking stage, covariance matching is employed to track the license plate. Genetic algorithm is used to reduce the computational cost. Real-time image tracking of multi-lane vehicles is achieved. In the experiment, test videos are recorded in advance by record- ers of actual E-police systems erage false detection rate and at several different city intersections. In the tracking module, the av- missed plates rate are 1.19%, and 1.72%, respectively.

YOLOv2 Deep Learning Model and GIS Based Algorithms for Vehicle Tracking

The latest advances in Deep Learning based methods and computational capabilities provide new opportunities for vehicle tracking. In this study, YOLOv2 (You Only Look Once—version 2) is used as an open source Convolutional Neural Network (CNN), to process high-resolution satellite images, in order to generate the spatio-temporal GIS (Geographic Information System) tracks of moving vehicles. At first step, YOLOv2 is trained with a set of images of 1024 × 1024 resolution from the VEDAI database. The model showed satisfactory results, with an accuracy of 91%, and then at second step, is used to process aerial images extracted from aerial video. The output vehicle bounding boxes have been processed and fed into the GIS based LinkTheDots algorithm, allowing vehicles identification and spatio-temporal tracks generation in GIS format.

Numerical Prediction of Ride Comfort of Tracked Vehicle Equipped with Novel Flexible Road Wheels

Enhancing ride comfort has always constituted a crucial focus in the design and research of modern tracked vehicles,heavily reliant on the driving system's performance.While the road wheel is a key component of the driving system,traditional road wheels predominantly adopt a solid structure,exhibiting subpar adhesion performance and damping effects,thereby falling short of meeting the demands for high-speed,stable,and long-distance driving in tracked vehicles.Addressing this issue,this paper proposes a novel type of flexible road wheel(FRW)characterized by a catenary construction.The study investigates the ride comfort of tracked vehicles equipped with flexible road wheels by integrating finite element and vehicle dynamic.First,three-dimensional(3D)finite element(FE)models of both flexible and rigid road wheels are established,considering material and contact nonlinearities.These models are validated through a wheel radial loading test.Based on the validated FE model,the paper uncovers the relationship between load and radial deformation of the road wheel,forming the basis for a nonlinear mathematical model.Subsequently,a half-car model of a tracked vehicle with seven degrees of freedom is established using Newton's second law.A random road model,considering the track effect and employing white noise,is constructed.The study concludes by examining the ride comfort of tracked vehicles equipped with flexible and rigid road wheels under various speeds and road grades.The results demonstrate that,in comparison to the rigid road wheel(RRW),the flexible road wheel enhances the ride comfort of tracked vehicles on randomly uneven roads.This research provides a theoretical foundation for the implementation of flexible road wheels in tracked vehicles.

High-speed tracked vehicle model order reduction for static and dynamic simulations

In this paper, a model order reduction strategy is adopted for the static and dynamic behaviour simulation of a high-speed tracked vehicle. The total number of degree of freedom of the structure is condensed through a selection of interface degrees of freedom and significant global mode shapes, for an approximated description of vehicle dynamic behaviour. The methodology is implemented in a customised open-source software to reduce the computational efforts. The modelled tracked vehicle includes the sprung mass, the unsprung masses, connected by means of torsional bars, and all the track assemblies, composing the track chain. The proposed research activity presents a comprehensive investigation of the influence of the track chain, combined with longitudinal vehicle speed, on statics and vehicle dynamics, focusing on vertical dynamics. The vehicle response has been investigated both in frequency and time domain. In this last case road-wheel displacements are assumed as inputs for the model, under different working conditions, hence considering several road profiles with different amplitudes and characteristic excitation frequencies. Simulation results have proven a high fidelity in model order reduction approach and a significant contribution of the track chain in the global dynamic behaviour of the tracked vehicle.

A hierarchical clustering of features approach for vehicle tracking in traffic environments

Purpose-Developing algorithms for automated detection and tracking of multiple objects is one challenge in the field of object tracking.Especially in a traffic video monitoring system,vehicle detection is an essential and challenging task.In the previous studies,many vehicle detection methods have been presented.These proposed approaches mostly used either motion information or characteristic information to detect vehicles.Although these methods are effective in detecting vehicles,their detection accuracy still needs to be improved.Moreover,the headlights and windshields,which are used as the vehicle features for detection in these methods,are easily obscured in some traffic conditions.The paper aims to discuss these issues.Design/methodology/approach-First,each frame will be captured from a video sequence and then the background subtraction is performed by using the Mixture-of-Gaussians background model.Next,the Shi-Tomasi corner detection method is employed to extract the feature points from objects of interest in each foreground scene and the hierarchical clustering approach is then applied to cluster and form them into feature blocks.These feature blocks will be used to track the moving objects frame by frame.Findings-Using the proposed method,it is possible to detect the vehicles in both day-time and night-time scenarios with a 95 percent accuracy rate and can cope with irrelevant movement(waving trees),which has to be deemed as background.In addition,the proposed method is able to deal with different vehicle shapes such as cars,vans,and motorcycles.Originality/value-This paper presents a hierarchical clustering of features approach for multiple vehicles tracking in traffic environments to improve the capability of detection and tracking in case that the vehicle features are obscured in some traffic conditions.

Vision-based Vehicle Tracking and Classification on the Highway

This paper presents algorithms for vision-based tracking and classification of vehicles in image sequences of traffic scenes recorded by a stationary camera. In the algorithms, the central moment and extended Kalman filter of tracking processes optimizes the amount of spent computational resources. Moreover, it robust to many difficult situations such as partial or full occlusions of vehicles. Vehicle classification performance is improved by Bayesian network, especially from incomplete data. The methods are test on a single Intel Pentium 4 processor 2.4 GHz and the frame rate is 25 frames/s. Experimental results from highway scenes are provided, which demonstrate the effectiveness and robust of the methods.

Shadow Extraction and Elimination of Moving Vehicles for Tracking Vehicles

Shadow extraction and elimination is essential for intelligent transportation systems(ITS)in vehicle tracking application.The shadow is the source of error for vehicle detection,which causes misclassification of vehicles and a high false alarm rate in the research of vehicle counting,vehicle detection,vehicle tracking,and classification.Most of the existing research is on shadow extraction of moving vehicles in high intensity and on standard datasets,but the process of extracting shadows from moving vehicles in low light of real scenes is difficult.The real scenes of vehicles dataset are generated by self on the Vadodara–Mumbai highway during periods of poor illumination for shadow extraction of moving vehicles to address the above problem.This paper offers a robust shadow extraction of moving vehicles and its elimination for vehicle tracking.The method is distributed into two phases:In the first phase,we extract foreground regions using a mixture of Gaussian model,and then in the second phase,with the help of the Gamma correction,intensity ratio,negative transformation,and a combination of Gaussian filters,we locate and remove the shadow region from the foreground areas.Compared to the outcomes proposed method with outcomes of an existing method,the suggested method achieves an average true negative rate of above 90%,a shadow detection rate SDR(η%),and a shadow discrimination rate SDR(ξ%)of 80%.Hence,the suggested method is more appropriate for moving shadow detection in real scenes.

Efficient Online Vehicle Tracking for Real-Virtual Mapping Systems

Multi-object tracking is a vital problem as many applications require better tracking approaches.Although learning-based detectors are becoming extremely powerful,there are few tracking methods designed to work with them in real time.We explored an efficient flexible online vehicle tracking-by-detection framework suitable for real-virtual mapping systems,which combines a non-recursive temporal window search with delayed output and produces stable trajectories despite noisy detection responses.Its computation speed meets the real-time requirements,whereas its performance is comparable with that of state-of-the-art online trackers on the DETRAC dataset.The trajectories from our approach also contain the target class and color information important for virtual vehicle motion reconstruction.

Vision-Based On-Road Nighttime Vehicle Detection and Tracking Using Taillight and Headlight Features

An important and challenging aspect of developing an intelligent transportation system is the identification of nighttime vehicles. Most accidents occur at night owing to the absence of night lighting conditions. Vehicle detection has become a vital subject for research to ensure safety and avoid accidents. New vision-based on-road nighttime vehicle detection and tracking system are suggested in this survey paper using taillight and headlight features. Using computer vision and some image processing techniques, the proposed system can identify vehicles based on taillight and headlight features. For vehicle tracking, a centroid tracking algorithm has been used. Euclidean Distance method has been used for measuring the distances between two neighboring objects and tracks the nearest neighbor. In the proposed system two flexible fixed Region of Interest (ROI) have been used, one is the Headlight ROI, and another is the Taillight ROI that could adapt to different resolutions of the images and videos. The achievement of this research work is that the proposed two ROIs can work simultaneously in a frame to identify oncoming and preceding vehicles at night. The segmentation techniques and double thresholding method have been used to extract the red and white components from the scene to identify the vehicle headlights and taillights. To evaluate the capability of the proposed process, two types of datasets have been used. Experimental findings indicate that the performance of the proposed technique is reliable and effective in distinct nighttime environments for detection and tracking of vehicles. The proposed method has been able to detect and track double lights as well as single light such as motorcycle light and achieved average accuracy and average processing time of vehicle detection about 97.22% and 0.01 s per frame respectively.

Evaluation of High-Speed Track Quality Using Dynamic Simulation of Vehicle-Track Interaction

Track quality is a determinant factor for evaluating the overall performance of vehicle track interaction with respect to safety, ride quality and maintenance. Important parameters specifying the general quality of the track include track geometry (undamped) and track stiffness (damped), which can be evaluated by measurements taken along with track sections. A new co-simulation model based on Finite Element Method (FEM) and Multi Body Simulation (MBS) is built for the detailed description of track quality and its contribution to vehicle track interaction without simplifying the track structure as interconnected single elements. The simulation models and tools have been validated with the help of measured track geometry, track stiffness and dynamic wheel rail forces along the track sections of high speed lines. A comparative study between high speed lines using conventional ballasted track and ballastless track showed a significantly better quality in ballastless track sections. The dynamic forces which were determined by simulations and verified by measurements along the ballastless track section were comparatively less than the specified limits by German regulations for ballastless track design. Lower levels of dynamic forces can be utilized for optimization of track design and installation procedures with respect to lower initial costs.

Dynamic Simulation of Tracked Vehicle Using the DADS Software

The track model used in the dynamic analysis and design system software is investigated. A home made tank is taken as an example to illustrate the method for modeling an integral tracked vehicle and perform the dynamic simulation. The obtained results have demonstrated that the simulation method has the advantage of high efficiency, more convenience and more insight into the dynamical behavior of the system.

NONLINEAR ESTIMATION METHODS FOR AUTONOMOUS TRACKED VEHICLE WITH SLIP

In order to achieve precise,robust autonomous guidance and control of a tracked vehicle,a kinematic model with longitudinal and lateral slip is established,Four different nonlinear filters are used to estimate both state vector and time-varying parameter vector of the created model jointly.The first filter is the well-known extended Kalman filter.The second filter is an unscented version of the Kalman filter.The third one is a particle filter using the unscented Kalman filter to generate the importance proposal distribution.The last one is a novel and guaranteed filter that uses a linear set-membership estimator and can give an ellipsoid set in which the true state lies.The four different approaches have different complexities,behavior and advantages that are surveyed and compared.

Turning traction force of tracked mining vehicle based on rheological property of deep-sea sediment

Based on main physical and mechanical properties of deep-sea sediment from C-C poly-metallic nodule mining area in the Pacific Ocean, the best sediment simulant was successfully prepared by mixing bentonite with a certain content of water. Compression-shear coupling rheological constitutive model of the sediment simulant was established by endochronic theory and the coupling rheological parameters were obtained by compressive and compression-shear creep tests. A new calculation formula of turning traction force of the tracked mining vehicle was first derived based on the coupling rheological model and consideration of pushing resistance and sinkage of the tracked mining vehicle. Effects of the turning velocity, crawler spacing and contacting length of crawler with deep-sea sediment on the turning traction force were analyzed. Research results can provide theoretical foundation for operation safety and optimal design of the tracked mining vehicle.

Predicting the sinkage of a moving tracked mining vehicle using a new rheological formulation for soft deep-sea sediment

The sinkage of a moving tracked mining vehicle is greatly af fected by the combined compression-shear rheological properties of soft deep-sea sediments. For test purposes, the best sediment simulant is prepared based on soft deep-sea sediment from a C-C poly-metallic nodule mining area in the Pacific Ocean. Compressive creep tests and shear creep tests are combined to obtain compressive and shear rheological parameters to establish a combined compressive-shear rheological constitutive model and a compression-sinkage rheological constitutive model. The combined compression-shear rheological sinkage of the tracked mining vehicle at dif ferent speeds is calculated using the Recur Dyn software with a selfprogrammed subroutine to implement the combined compression-shear rheological constitutive model. The model results are compared with shear rheological sinkage and ordinary sinkage(without consideration of rheological properties). These results show that the combined compression-shear rheological constitutive model must be taken into account when calculating the sinkage of a tracked mining vehicle. The combined compression-shear rheological sinkage decrease with vehicle speed and is the largest among the three types of sinkage. The developed subroutine in the Recur Dyn software can be used to study the performance and structural optimization of moving tracked mining vehicles.

A Study on the Dynamic Analysis of A Tracked Vehicle for Ocean Mining on the Deep Seabed

A study is presented on the dynamic analysis of a tracked vehicle for mining on the deep seabed of very soft soil. Equations for the interaction between the track and extremely soft seabed are employed to develop a track/soil interaction module called TVAS. The vehicle is modeled as a multibody dynamic system by the use of a multibody dynamic analysis program. The module developed is cooperated with the multibody dynamic analysis program with a user-defined subroutine. The dynamic behavior and the conceptual design of the mining vehicle on the deep seabed are investigated.

Configuration design and control of hybrid tracked vehicle with three planetary gear sets

The hybrid tracked vehicles(HTV)usually adopt series hybrid powertrain with extra steering mechanism,which has relatively low transmission efficiency and reduces the flexibility of structural arrangement.To overcome the disadvantages,a new kind of single-mode powertrain has been proposed.The power-split hybrid powertrain is composed of three planetary gear(PG)sets connected to one engine,left and right track outputs,and three motors.The proposed powertrain can realize steering while going forward by controlling the output torque on each side without extra steering mechanism or steering shaft.Due to the diversity of the connection way between components and planetary gear sets,a rapid configuration design approach is proposed for the design selection of HTV.The automated dynamic modelling method can show the one-to-one correspondence with the selected feasible groups by establishing two characteristic matrices,which is more simple than other researches.The analytically-based method is proposed to classify all possible connection designs into several groups to decrease the searching scope with improved design efficiency.Finally,the optimal control strategy is used to find the design with optimal fuel economy under typical condition of HTV.The case study is implemented by the proposed design approach which demonstrates better design performances compared with the existing series-hybrid HTV.

Study on Steering Control Strategy for High-Speed Tracked Vehicle with Hydrostatic Drive

Steering control strategy for high-speed tracked vehicle with hydrostatic drive is designed based on analyzing the fundamental steering theories of the hydrostatic drive tracked vehicle. The strategy is completed by the cooperation between integrated steering control unit and pump ＆ motor displacement controller. The steering simulation is conducted by using Simulink of Matlab. It is indicated that this steering control strategy can reduce the average vehicle speed automatically to achieve the driver＇s expected steering radius exactly in the case of en- suring not exceeding the system pressure threshold and no sideslip.

Neuron PI control for semi-active suspension system of tracked vehicle

A neuron proportion integration (PI) control strategy for semi-active suspension system of tracked vehicle was proposed based on its unique structure and the multiple and complex environment of the driving traffic. An adaptive genetic algorithm is used to optimize the parameters of the neuron PI controller. The simulation result of the neuron PI control for semi-active suspension system of tracked vehicle indicates that the vertical amplitude,pitch angle and vertical acceleration of the vehicle are well controlled. The root mean square (RMS) of the vertical amplitude decreases by 37.2%,and 45.2% for the pitch angle,38.6% for the vertical acceleration. The research of neuron PI control experiment for the semi-active suspension system of the tracked vehicle model mining in benthal indicates that the RMS of the weight acceleration vibrating along the vertical direction decreases by 29.5%,the power spectral density resonance peak of the acceleration of the car body decreases by 23.8%.

Nonlinear Derivative and Integral Sliding Control for Tracked Vehicle Steering with Hydrostatic Drive

In the steering process of tracked vehicle with hydrostatic drive,the motion and resistance states of the vehicle are always of uncertain and nonlinear characteristics,and these states may undergoe large-scale changes.Therefore,it is significant to enhance the steering stability of tracked vehicle with hydrostatic drive to meet the need of future battlefield.In this paper,a sliding mode control algorithm is proposed and applied to achieve desired yaw rates.The speed controller and the yaw rate controller are designed through the kinematics and dynamics analysis.In addition,the nonlinear derivative and integral sliding mode control algorithm is designed,which is supposed to efficiently reduce the integration saturation and the disturbances from the unsmooth road surfaces through a conditional integrator approach.Moreover,it improves the response speed of the system and reduces the chattering by the derivative controller.The hydrostatic tracked vehicle module is modeled with a multi-body dynamic software RecurDyn and the steering control strategy module is modeled by MATLAB/Simulink.The co-simulation results of the whole model show that the control strategy can improve the vehicle steering response speed and also ensure a smooth control output with small chattering and strong robustness.