Tracking Stage Research Articles

Efficient and robust segmentation and tracking of sperm cells in microscopic image sequences

Sperm motility analysis is an important factor in male fertility diagnosis. This article presents a hybrid segmentation method to detect sperm cells, which is robust to density variation of the cells in the image sequences. In addition, a preprocessing scheme is employed to remove fixed sperm cells and debris, which facilitate and speed up the cells' tracking stage. The article also proposes an automated sperm‐tracking algorithm in semen samples image sequences. It is a multi‐step tracking scheme, which is an enhanced version of adaptive window average speed (AWAS) tracking algorithm. It retrieves lost sperm cells during the tracking stage in adjacent frames and alleviates the cells collide problem. The proposed tracking algorithm provides both superior accuracy and higher speed compared to those of the other competitive algorithms for image sequences regardless of their particle densities.

A hybrid gray wolf and genetic whale optimization algorithm for efficient moving object analysis

Object detection in realistic situations needs various essential applications. The foremost applications of machine vision like vision based monitoring system, object tracking etc. require background subtraction (BS) complied with identification of motion objects. Segregating forefront from background is a challenging task in videos discovered through motion cam since either forefront or background relevant information varies in each successive frame of the video series; hence a pseudo-motion is sensitive with background. Modified Kernel fuzzy c-means technique still bears a few drawbacks, for example, decreased convergence rate, acquiring stuck in the local minima and also in risk to instatement level of sensitivity. To overcome the above problems, here we recommend a technique for information clustering utilizing the OWC (Optimal Weighted Centroid) procedure that decides the optimum centroid for playing out the clustering procedure. To overcome the issues, here we recommend a technique Optimal Background separation using Optimal Weighted Centroid (OWC) - Modified Kernel Fuzzy C Means Algorithm (MKFCM) for information clustering utilizing the OWC (Optimal Weighted Centroid) procedure that decides the optimum centroid for playing out the clustering procedure. The OWC procedure makes use of the procedural activities of the Whale Optimization algorithm (WOA) with the fusion of the Grey Wolf Optimization (GWO). The prescribed new procedure is dynamic clustering technique for splitting up of moving object. Moving object tracking is accomplished through the blob detection which comes under the tracking stage. The examination stage has attribute extraction and also classification. Appearance-based as well as high quality based attributes are drawn out from the refined frames which are provided for classification. Since classification we are making use of J48 (C4.5) i.e., decision tree based classifier. The efficiency of the recommended strategy is examined with preceding strategies k-NN as well as MLP in regard to accuracy, f-measure, ROC as well as recall.

IMAGE-BASED VEHICLE TRACKING FROM ROADSIDE LIDAR DATA

Abstract. Vehicle tracking is of great importance in urban traffic systems, and the adoption of lidar technologies – including on-board and roadside systems – has significant potential for such applications. This research therefore proposes and develops an image-based vehicle-tracking framework from roadside lidar data to track the precise location and speed of a vehicle. Prior to tracking, vehicles are detected in point clouds through a three-step procedure. Cluster tracking then provides initial tracking results. The second tracking stage aims to provide more precise results, in which two strategies are developed and tested: frame-by-frame and model-matching strategies. For each strategy, tracking is implemented through two threads by converting the 3D point cloud clusters into 2D images relating to the plan and side views along the tracked vehicle’s trajectory. During this process, image registration is exploited in order to retrieve the transformation parameters between every image pair. Based on these transformations, vehicle speeds are determined directly based on (a) the locations of the chosen tracking point in the first strategy; (b) a vehicle model is built and tracking point locations can be calculated after matching every frame with the model in the second strategy. In contrast with other existing methods, the proposed method provides improved vehicle tracking via points instead of clusters. Moreover, tracking in a decomposed manner provides an opportunity to cross-validate the results from different views. The effectiveness of this method has been evaluated using roadside lidar data obtained by a Robosense 32-line laser scanner.

Artificial vision assisted ground fine pointing system for experimental optical link for CubeSat communications

Considering the continuous increase of demands in satellite communications, it is imperative to determine systems with higher bandwidths. Furthermore, miniaturization trends coming from the development of nanosatellites as CubeSat’s, constitute great restrictions to their design. Optical communications have the potential to lead with current data rates requirements. Nevertheless, the establishment of ground-LEO (Low Earth Orbit) optical links poses several challenges such as very strict and accurate tracking mechanisms, effects provoked due to the environmental conditions on the light beam as well as attenuation and Doppler effects. In this work, the precision of the tracking mechanisms is tackled by employing artificial vision as a proposal for a fine tracking system for an optical ground station to be able to locate a LEO CubeSat, so to proceed with data acquisition and tracking stages. The innovative and highly efficient algorithm herein developed for fine pointing is implemented in LabVIEW® as an example.

Object Tracking Based on Vector Convolutional Network and Discriminant Correlation Filters.

Due to the fast speed and high efficiency, discriminant correlation filter (DCF) has drawn great attention in online object tracking recently. However, with the improvement of performance, the costs are the increase in parameters and the decline of speed. In this paper, we propose a novel visual tracking algorithm, namely VDCFNet, and combine DCF with a vector convolutional network (VCNN). We replace one traditional convolutional filter with two novel vector convolutional filters in the convolutional stage of our network. This enables our model with few memories (only 59 KB) trained offline to learn the generic image features. In the online tracking stage, we propose a coarse-to-fine search strategy to solve drift problems under fast motion. Besides, we update model selectively to speed up and increase robustness. The experiments on OTB benchmarks demonstrate that our proposed VDCFNet can achieve a competitive performance while running over real-time speed.

Coherent Detection and 3D Tracking Stages of a DVB-T Based Passive Radar for Terrestrial Traffic Monitoring

This work tackles terrestrial traffic monitoring using a Passive Radar System (PRS) characterized by a surveillance channel processing composed of a Neyman-Pearson (NP) coherent detector and three-dimensional tracker implementation. The NP detector is a parametric approach that requires the prior knowledge of target and bistatic clutter statistical models at communication systems frequencies that fit with real data. The proposed tracker provides three-dimensional (range, azimuth and speed) trajectories declaration exploiting the capabilities of beamforming techniques based on a uniform linear array as surveillance antenna system. The considered solution is validated with real data acquired by a PRS in a semi-urban scenario.

Real-time tumor tracking using fluoroscopic imaging with deep neural network analysis

PurposeTo improve respiratory gating accuracy and treatment throughput, we developed a fluoroscopic markerless tumor tracking algorithm based on a deep neural network (DNN). MethodsIn the learning stage, target positions were projected onto digitally reconstructed radiography (DRR) images from four-dimensional computed tomography (4DCT). DRR images were cropped into subimages of the target or surrounding regions to build a network that takes input of the image pattern of subimages and produces a target probability map (TPM) for estimating the target position. Using multiple subimages, a DNN was trained to generate a TPM based on the target position projected onto the DRRs. In the tracking stage, the network takes in the subimages cropped from fluoroscopic images at the same position of the subimages on the DRRs and produces TPMs, which are used to estimate target positions. We integrated the lateral correction to modify an estimated target position by using a linear regression model. We tracked five lung and five liver cases, and calculated tracking accuracy (Euclidian distance in 3D space) by subtracting the estimated position from the reference. ResultsTracking accuracy averaged over all patients was 1.64 ± 0.73 mm. Accuracy for liver cases (1.37 ± 0.81 mm) was better than that for lung cases (1.90 ± 0.65 mm). Computation time was <40 ms for a pair of fluoroscopic images. ConclusionsOur markerless tracking algorithm successfully estimated tumor positions. We believe our results will provide useful information to advance tumor tracking technology.

A novel global maximum power point tracking algorithm for photovoltaic system with variable perturbation frequency and zero oscillation

By combing the advantages of two widely-used methods together, namely the particle swarm optimization (PSO) and the perturb and observe (PO), this paper develops and discusses a novel GMPPT algorithm with fast tracking speed for different partial shading patterns and zero steady-state oscillation. Three key elements that form the foundation of the proposed algorithm have been discussed: (1) variable perturbation frequency (VPF) to speed up the dynamic tracking speed; (2) PO tracking is used after convergence of PSO to tackle the reinitialization problem of the PSO since PSO is often unable to detect the small change in the PV system; (3) fixed duty cycle is implemented for the steady state with the PO to realize the zero oscillation. Different partial shading patterns and varying irradiance scenarios are used to evaluate the transient as well as steady state operation of the algorithm. Both simulation and experimental results show that the VPF can greatly reduce the duration of the searching interval of the PSO tracking stage while the following PO tracking stage ensure the correct detection of the proposed algorithm for small changes in the PV system and zero oscillation for the steady state operation.

Fixing the Leaky Pipeline: Strategies for Making Economics Work for Women at Every Stage

While women comprise over half of all undergraduate students in the United States, they account for less than one-third of economics majors. From there, the proportion of women at each stage of the academic tenure track continues to decrease, creating a “leaky pipeline.” In this paper, I provide a toolkit of interventions that could be implemented by individuals, organizations, or academic units who are working to attract and retain women students and faculty at each stage of this pipeline. I focus on smaller-scale, targeted interventions that have been evaluated in a way that allows for the credible estimation of causal effects.

An Algorithm for Tracking Multiple Fish Based on Biological Water Quality Monitoring

Abnormal water quality will increase the occlusion rate among fish schools, which causes difficulties in fish detection and tracking. In order to solve this problem, a multiple fish tracking algorithm for red snapper is proposed in this paper. In the detection stage, we use the Otsu adaptive segmentation algorithm to extract fish targets based on the background subtraction method, following which the fish tracking feature parameters can be obtained based on the fish geometric features. In the tracking stage, the Kalman filter is employed to first estimate the motion state, and then the cost function is constructed from the position of the fish body, target area, and the direction information. Finally, fish school tracking is realized by the interframe relationship matrix. We applied several tracking methods with various schemes to experimental videos of swimming fish schools in different environments. The experimental results show that the proposed tracking algorithm exhibits improved performance and robustness.

Dynamic Adaptive Hybrid Impedance Control for Dynamic Contact Force Tracking in Uncertain Environments

The importance of robot contact operation control has been increasing recently due to a need for robots to interact more with the outside world. However, traditional robot compliance control cannot take both transient contact force overshoots and steady-state force tracking error problems into account. To address this problem, this paper aims to design a dynamic adaptive hybrid impedance (DAHI) controller to deal with dynamic contact force tracking in uncertain environments (e.g., polishing scenarios). Under the premise of analyzing the transient response and steady-state error in the hybrid impedance control (HI) and adaptive hybrid impedance (AHI) control, the DAHI control, which combines the advantages from HI and AHI control, is applied to improve the performance of AHI controller. The main goal of such a controller is to avoid force overshoots in the contact stage while maintaining force tracking error in the dynamic tracking stage. The proposed controller is capable of adapting its update rate parameter online in order to track a reference force in uncertain environments. Besides, it does not require any modeling or estimation of an environment's dynamics or the robot's dynamics. The simulation and experimental results both show the achieved control performance. The results have also been compared with the previous control methods.

An Application of a Special Method for Designating the Control Forces for the Homing of an Anti-Aircraft Missile on an Aerial Target by Use of a Method of Proportional Navigation

An analysis of application feasibility of a special method for surface-to-air missile (SAM) control during homing on an airborne target was carried out in this paper. A prior implementation of the method was motion control for a gyroscope axis with three degrees of freedom [1, 2] at the stages of spatial seeking and tracking of a detected target. The positive results obtained during that research led to a conclusion that the proposed control method would be appropriate for the determination of the control forces for missile guidance. This method consists of the application of the phase trajectories of control errors. Switching over of the control forces at suitable phase plane points reduced the control errors to zero and facilitated a proper flight path of the SAM. This paper presents a switching algorithm, equations of the kinematics and dynamics of SAM flight, and a number of examples of numerical simulations of the problem contemplated herein. The simulation results were represented in a graphic format.

Moving target ranging method for a photon-counting system.

The photon-counting technique is finding increasing applications in laser ranging systems owing to its high detection sensitivity and time resolution; however, it is still challenging to extract moving tracks accurately from an intensive noise background for non-cooperative targets using the photon-counting technique. To resolve this issue, a ranging method based on the Hough transform is proposed according to the distribution characteristics of a moving target in a point cloud figure. The proposed method divides the ranging process into the initial stage and the tracking stage. A large amount of point cloud data is used to acquire an accurate measurement of the initial distance and velocity of the target in the initial stage, whereas a smaller amount of point cloud data is used to rapidly update the distance and velocity for real-time processing in the tracking stage. The experimental results demonstrated that compared with the local distance statistics method and the density-based filtering method, the proposed method could extract target tracks effectively with decreased time consumption and lower ranging errors in different detection conditions.

Deep Directional Network for Object Tracking

Existing object trackers are mostly based on correlation filtering and neural network frameworks. Correlation filtering is fast but has poor accuracy. Although a neural network can achieve high precision, a large amount of computation increases the tracking time. To address this problem, we utilize a convolutional neural network (CNN) to learn object direction. We propose a target direction classification network based on CNNs that has a directional shortcut to the tracking target, unlike the particle filter that randomly finds the target. Our network uses an end-to-end approach to determine scale variation that has good robustness to scale variation sequences. In the pretraining stage, the Visual Object Tracking Challenges (VOT) dataset is used to train the network for learning positive and negative sample classification and direction classification. In the online tracking stage, the sliding window operation is performed by using the obtained directional information to determine the exact position of the object. The network only calculates a single sample, which guarantees a low computational burden. The positive and negative sample redetection strategies can successfully ensure that the samples are not lost. The one-pass evaluation (OPE) evaluation results of the object tracking benchmark (OTB) demonstrate that the algorithm is very robust and is also faster than several deep trackers.

Trajectory planning and optimisation method for intelligent vehicle lane changing emergently

The real time and adaptability of emergency lane-changing trajectory planning method is critical to the safe operation of the intelligent vehicle. This study presents the design and implementation of the trajectory planning and optimisation method of intelligent vehicle lane changing emergently based on hp-adaptive pseudospectral method. The method divides the emergency lane-changing process into the initial stage and tracking stage for trajectory planning based on road steering experiment and sigmoid functions. An hp-adaptive pseudospectral method is introduced to connect and optimise the lane-changing trajectory. PreScan and Matlab are used to simulate the planning of emergency lane-changing trajectory under six conditions, and the simulation results verify the effectiveness and real time of the planning and optimisation method. In comparison with trajectory planning methods based on polynomial functions, this method is characterised by shorter response time and safety distance and has better adaptability under different conditions.

Automated Cell Tracking Using Motion Prediction-Based Matching and Event Handling.

Automated cell segmentation and tracking enables the quantification of static and dynamic cell characteristics and is significant for disease diagnosis, treatment, drug development, and other biomedical applications. This paper introduces a method for fully automated cell tracking, lineage construction, and quantification. Cell detection is performed in the joint spatio-temporal domain by a motion diffusion-based Partial Differential Equation (PDE) combined with energy minimizing active contours. In the tracking stage, we adopt a variational joint local-global optical flow technique to determine the motion vector field. We utilize the predicted cell motion jointly with spatial cell features to define a maximum likelihood criterion to find inter-frame cell correspondences assuming Markov dependency. We formulate cell tracking and cell event detection as a graph partitioning problem. We propose a solution obtained by minimization of a global cost function defined over the set of all cell tracks. We construct a cell lineage tree that represents the cell tracks and cell events. Finally, we compute morphological, motility, and diffusivity measures and validate cell tracking against manually generated reference standards. The automated tracking method applied to reference segmentation maps produces an average tracking accuracy score ( TRA) of 99 percent, and the fully automated segmentation and tracking system produces an average TRA of 89 percent.

Novel welding machine based on small PMSG wind turbine

This paper investigates a small wind turbine based on a permanent magnet synchronous generator (PMSG) to replace the used diesel generator in welding machines at remote sites. The proposed welding machine is designed using a variable-speed PMSG wind turbine to achieve an optimal performance. The problem of fluctuation in wind energy is solved by adding a storage battery and a maximum power point tracking (MPPT) stage. A controllable direct current power supply based on the full-bridge inverter topology is adopted for the welding side. The control strategies of MPPT, pitch angle blade, and arc welding current are also discussed. Matlab/Simulink is utilized to simulate the proposed system. Simulation results show that the proposed welding machine can provide a stable and robust welding current under varying wind and arc load conditions. Simulation results demonstrate the feasibility and efficiency of the proposed welding machine.

Unambiguous Tracking Technique Based on Combined Correlation Functions for Sine BOC Signals

A new unambiguous tracking technique based on combined correlation functions for sine Binary Offset Carrier (BOC) signals is proposed in this paper. The key to this method is to exploit two types of local reference signals: the BOC signal and a linear combination of a series of BOC signals with different delays. They are both correlated with the received signals. Then, a correlation function without any positive side peaks is obtained by multiplying the two correlation results to make tracking completely unambiguous. Theoretical analysis and simulation in the tracking stage show that the proposed method has the best code tracking accuracy among the method tracking BOC signals like Binary Phase-Shift Keying signals (BPSK-LIKE), the Pseudo correlation function based Unambiguous Delay Lock Loop (PUDLL), Symmetrical Pulse Ambiguity Removing (SPAR) technique, the method proposed by Shen Feng (SF) and the two methods proposed by Yan Tao (YT-V1 and YT-V2). In multipath environments, the proposed method has the best anti-multipath performance of all the tracking methods mentioned above. In conclusion, the proposed method can completely eliminate ambiguity and has significant performance advantages compared with the methods mentioned above.

Objective evaluation of ram and buck sperm motility by using a novel sperm tracker software.

This work offers researchers the first version of an open-source sperm tracker software (Sperm Motility Tracker, V1.0) containing a novel suit of algorithms to analyze sperm motility using ram and buck sperm as models. The computer-assisted semen analysis is used in several publications with increasing trend worldwide in the last years, showing the importance of objective methodologies to evaluate semen quality. However, commercial systems are costly and versatility is constrained. In the proposed method, segmentation is applied and the tracking stage is performed by using individual Kalman filters and a simplified occlusion handling method. The tracking performance in terms of precision (number of true tracks), the percentage of fragmented paths and percentage of correctly detected particles were manually validated by three experts and compared with the performance of a commercial motility analyzer (Microptic's SCA). The precision obtained with our sperm motility tracker was higher than the one obtained with a commercial software at the current acquisition frame rate of 25 fps (P < 0.0001), concomitantly with a similar percentage of fragmentized tracks (P = 0.0709) at sperm concentrations ranging 25-37 × 106 cells/mL. Moreover, our tracker was able to detect trajectories that were unseen by SCA. Kinetic values obtained by using both methods were contrasted. The higher values found were explained based on the better performance of our sperm tracker to report speed parameters for very fast motile sperm. To standardize results, acquisition conditions are suggested. This open-source sperm tracker software has a good plasticity allowing researchers to upgrade according requirements and to apply the tool for sperm from a variety of species.

An extended Kalman filter for mouse tracking.

Animal tracking is an important tool for observing behavior, which is useful in various research areas. Animal specimens can be tracked using dynamic models and observation models that require several types of data. Tracking mouse has several barriers due to the physical characteristics of the mouse, their unpredictable movement, and cluttered environments. Therefore, we propose a reliable method that uses a detection stage and a tracking stage to successfully track mouse. The detection stage detects the surface area of the mouse skin, and the tracking stage implements an extended Kalman filter to estimate the state variables of a nonlinear model. The changes in the overall shape of the mouse are tracked using an oval-shaped tracking model to estimate the parameters for the ellipse. An experiment is conducted to demonstrate the performance of the proposed tracking algorithm using six video images showing various types of movement, and the ground truth values for synthetic images are compared to the values generated by the tracking algorithm. A conventional manual tracking method is also applied to compare across eight experimenters. Furthermore, the effectiveness of the proposed tracking method is also demonstrated by applying the tracking algorithm with actual images of mouse. Graphical abstract.