Multiple Hypothesis Tracking Algorithm Research Articles

A New Scatterer Trajectory Association Method for ISAR Image Sequence Utilizing Multiple Hypothesis Tracking Algorithm

Scatterer trajectory association is a critical step of 3-D target reconstruction from the inverse synthetic aperture radar (ISAR) image sequence. To cope with the complex scatterer trajectory association of the noncooperative target, a novel method based on multiple hypothesis tracking (MHT) algorithm is proposed. First, the scatterer trajectory association situation is modeled as a multiple hypothesis tree, in which each branch represents a possible association. Then, to generate the hypothesis in each specific branch, a general trajectory motion model is constructed and the parameters are estimated based on the current trajectory association situation. The parameter estimation precision will increase with the growth of the trajectory length. Besides, to eliminate the influence of inaccurate association initialization and direction selection, a fusion algorithm is proposed to merge the forward and backward associated trajectories. Finally, experimental results based on the simulated data and electromagnetic data verify the effectiveness and robustness of the proposed method.

Multistatic Passive Radar Multi-target Tracking Under Target-measurement-illuminator Data Association Uncertainty

Different from the traditional multi-target tracking problem which has the measurements to targets data association uncertainty problem, the multistatic passive radar multi-target tacking system has the additional measurements to illuminators data association uncertainty problem, which means the data association relationship is three dimensional. A novel target-measurement-illuminator Probabilistic Multiple Hypothesis Tracking (PMHT) algorithm is proposed, which introduces a new data association variable to represent the data association relationship. The proposed algorithm is based on the Expectation-Maximization (EM). To handle the nonlinear problem of range-Doppler measurements, the Unscented Kalman Smoother (UKS) is used to get the multi-targets’ estimated states. To increase the data association accuracy, the measurements are set to mixture Gaussian distribution. Simulation results show that for the FKIE passive radar data set, the proposed algorithm can track multi-targets effectively in dense clutter environment.

Machine learning-based ship detection and tracking using satellite images for maritime surveillance

The recent advancement in remote sensing technologies has resulted in the availability of different imaging modes and higher resolution satellite images. Accessibility of these remote sensing or satellite images, automatic ship detection and tracking has become an important research topic in the field of maritime surveillance. In this paper, a novel method for ship detection using satellite images is proposed. First the preprocessing is carried out to remove the noise from the images using Ship Detection and Tracking (SDT) filter. Then, the land masking (sea-land area separation) and cloud masking is carried out based on the gradient feature extraction using SDT edge detection, along with SDT segmentation. Finally, the ships are identified using the Machine Learning (ML) classifiers like Support Vector Machine (SVM), Random Forest Classifier (RFC), Linear Discriminant Analysis (LDA), Logistic Regression (LR), KNN, and Gaussian Naïve Bayes-based classifier based on the features extracted from Histogram of Oriented Gradients (HOG). The proposed work is cross validated using the Google earth data. Performance of our proposed method is evaluated using the recall and the precision values. Further, for tracking ships, an improved multiple hypothesis tracking (MHT) algorithm is proposed and tested using the Kaggle dataset.

Improved N - Best Multiple Hypothesis Tracking Algorithm

In the tracking process of the multi-hypothesis tracking algorithm, many hypothesis are made about the correlation between the track and the measured values. Although its tracking performance is better than other tracking algorithms, it needs a lot of computation time and memory resources when tracking multiple targets or in environments with large clutter due to exponential growth of hypothesis. In view of this situation, an improved N-Best algorithm is proposed to be applied to MHT. The simulation results show that this method can reduce the computation time and realize the track correlation, so it has certain practicability.

Cyclist detection and tracking based on multi-layer laser scanner

The technology of Artificial Intelligence (AI) brings tremendous possibilities for autonomous vehicle applications. One of the essential tasks of autonomous vehicle is environment perception using machine learning algorithms. Since the cyclists are the vulnerable road users, cyclist detection and tracking are important perception sub-tasks for autonomous vehicles to avoid vehicle-cyclist collision. In this paper, a robust method for cyclist detection and tracking is presented based on multi-layer laser scanner, i.e., IBEO LUX 4L, which obtains four-layer point cloud from local environment. First, the laser points are partitioned into individual clusters using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method based on subarea. Then, 37-dimensional feature set is optimized by Relief algorithm and Principal Component Analysis (PCA) to produce two new feature sets. Support Vector Machine (SVM) and Decision Tree (DT) classifiers are further combined with three feature sets, respectively. Moreover, Multiple Hypothesis Tracking (MHT) algorithm and Kalman filter based on Current Statistical (CS) model are applied to track moving cyclists and estimate the motion state. The performance of the proposed cyclist detection and tracking method is validated in real road environment.

Underwater Bearing-Only Multitarget Tracking in Dense Clutter Environment Based on PMHT

Underwater bearing-only multitarget tracking in clutter environment is challenging because of the measurement nonlinearity, range unobservability, and data association uncertainty. In terms of the principle of expectation maximization, combining the extended Kalman filter (EKF) and unscented Kalman filter algorithm(UKF), a new bearing-only multi-sensor multitarget tracking via probabilistic multiple hypothesis tracking(PMHT) algorithm is proposed. The PMHT algorithm introduces an association variable to deal with the data association uncertainty problem between the measurements and the targets. Furthermore, the EKF-based PMHT for multi-sensor multitarget system is simplified, which obviate the need to "stack" the synthetic measurements and can reduce the computation cost. The estimation accuracy of the EKF based on PMHT approach and UKF based on PMHT approach in simulation experiments for underwater bearing-only cross-moving targets and closely spaced targets for the case of stationary multiple observations and maneuvering single observation under dense clutter environment is analyzed. The experimental results demonstrate that the present algorithm is very well in a highly clutter environment and its computational load is low, which confirms the effectiveness of the algorithm to the bearing-only multitarget tracking in dense clutter.

A Novel Global Localization Approach Based on Structural Unit Encoding and Multiple Hypothesis Tracking

In this paper, we present a novel 2-D laser-based global localization approach for mobile robots, which is composed of geometrical relationship construction, a new structural unit encoding scheme (SUES), and an extended multiple hypothesis tracking (MHT) algorithm. Different from existing methods, we construct a 3-D “directional endpoint” feature encapsulating both the endpoint and the direction of a line segment; on this basis, a novel and efficient online structural unit encoding scheme (SUES) is proposed to describe the geometric relationship between the two directional endpoint features with some robustness to dynamic disturbances. Note that SUES presented in this paper is different from the bag-of-words scheme in two aspects: 1) SUES quantizes the geometrical relationship without offline training for vocabulary and 2) SUES is independent of the quality of the vocabulary. By factoring the global localization problem into a discrete pose estimation problem, the MHT is extended on the basis of SUES and odometry information to gradually restore the global robot pose. Different from the classical MHT framework, the extended MHT takes the independent observation results as the a priori , while the likelihood term is composed of consecutive candidate poses and the odometry information. Evaluations are carried out by using both publicly available data sets and self-recorded data sets. Comparative experimental results with respect to the adaptive Monte Carlo localization are presented to show the superior performance of the proposed approach in terms of success ratio and efficiency.

Optimised multi‐hypothesis tracking algorithm based on the two‐dimensional constraints and manoeuvre detection

With the increase of target mobility and environment complexity, highly reliable single-period statistical judgments become more and more difficult. The multiple hypothesis tracking (MHT) algorithm is a method based on delay logic, and can effectively solve the problem of data association in the course of tracking. However, the number of hypotheses generated by the MHT algorithm is exponentially related to the false alarm rate and the number of targets. Therefore, the hypotheses reduction techniques are required for the implementation of the algorithm. The N-scan-back method and K-best method are often used. On the basis of understanding the K-Best method and the N-Scan-back method, this study proposes the two-dimensional constraints by the K-Best method and the N-Scan-back method, and besides adds the target manoeuvre detection method to jointly manage the hypotheses based on the mean of the filter residual. According to the manoeuvre detection results, the optimised MHT algorithm can adjust the likelihood probability calculation model of the plot-track association and the posterior probability calculation model of the hypothetical branch track, and shorten the decision depth N in the N-Scan-back method. Through simulation, it is proved that the optimised algorithm can reduce the calculation amount and improve the tracking accuracy.

Multiple hypothesis tracking algorithm for multi‐target multi‐camera tracking with disjoint views

In this study, a multiple hypothesis tracking (MHT) algorithm for multi-target multi-camera tracking (MCT) with disjoint views is proposed. Our method forms track-hypothesis trees, and each branch of them represents a multi-camera track of a target that may move within a camera as well as move across cameras. Furthermore, multi-target tracking within a camera is performed simultaneously with the tree formation by manipulating a status of each track hypothesis. Each status represents three different stages of a multi-camera track: tracking, searching, and end-of-track. The tracking status means targets are tracked by a single camera tracker. In the searching status, the disappeared targets are examined if they reappear in other cameras. The end-of-track status does the target exited the camera network due to its lengthy invisibility. These three status assists MHT to form the track-hypothesis trees for multi-camera tracking. Furthermore, they present a gating technique for eliminating of unlikely observation-to-track association. In the experiments, they evaluate the proposed method using two datasets, DukeMTMC and NLPR-MCT, which demonstrates that the proposed method outperforms the state-of-the-art method in terms of improvement of the accuracy. In addition, they show that the proposed method can operate in real-time and online.

A Novel Ship-Tracking Method for GF-4 Satellite Sequential Images.

The geostationary remote sensing satellite has the capability of wide scanning, persistent observation and operational response, and has tremendous potential for maritime target surveillance. The GF-4 satellite is the first geostationary orbit (GEO) optical remote sensing satellite with medium resolution in China. In this paper, a novel ship-tracking method in GF-4 satellite sequential imagery is proposed. The algorithm has three stages. First, a local visual saliency map based on local peak signal-to-noise ratio (PSNR) is used to detect ships in a single frame of GF-4 satellite sequential images. Second, the accuracy positioning of each potential target is realized by a dynamic correction using the rational polynomial coefficients (RPCs) and automatic identification system (AIS) data of ships. Finally, an improved multiple hypotheses tracking (MHT) algorithm with amplitude information is used to track ships by further removing the false targets, and to estimate ships’ motion parameters. The algorithm has been tested using GF-4 sequential images and AIS data. The results of the experiment demonstrate that the algorithm achieves good tracking performance in GF-4 satellite sequential images and estimates the motion information of ships accurately.

Joint Kinematic and Feature Tracking of Ships with Satellite Electronic Information

To track multiple ships and estimate the feature parameters of multiple emitters on board using electronic intelligence satellites under clutter interference, a long and random revisit time, and other complex conditions, a novel tracking algorithm using both kinematic (position and velocity) and feature information based on an improved Multiple Hypothesis Tracking (MHT) approach is proposed in this paper. Firstly, the characteristics of multi-ship tracking with multiple emitters using satellite electronic information are analysed, and a new model of an emitter is built as an extended target in geographical coordinates. Secondly, a pre-processing of measurements is utilised via hierarchical clustering using the location and feature information of emitters. Thirdly, feature information is incorporated into the MHT framework using Jensen-Shannon divergence distance and fuzzy C-means clustering to calculate track scores. Finally, we present the prediction and update of target states, especially the update of feature parameters, to realise joint kinematic and feature tracking of ships. The results of the simulation show that the proposed method has much better tracking performance than the standard MHT algorithm.

A novel fast target tracking method for UAV aerial image

Abstract Unmanned aerial vehicles (UAV) are able to achieve autonomous flight without drivers, and UAV has been a key tool to extract space data. Therefore, how to detect the trajectories of targets from UAV aerial image sequences is of great importance. Because local features are suitable to detect target tracking, we exploit scaleinvariant feature transform (SIFT) features to describe the interesting keypoints of targets. The main innovation of this paper is to utilize Multiple hypothesis tracking (MHT) algorithm to track an object (target) in a series of image sequences. Particularly, we develop a MHT framework based on a multidimensional assignment formulation and a sliding time window policy. To obtain target tracking from UAV aerial image sequences, three steps should be done, that is, 1) Breaking each track set into tracklet at a specific time, 2) Estimating the association cost of each track set, 3) Merging trajectory fragments to a longer one iteratively. Finally, we collect several UAV aerial image sequences with different target density to construct a dataset, and experimental results demonstrate the effectiveness of the proposed algorithm.

Book review: Review of Particle Filters for Random Set Models

Branko Ristic has given us a superb new book that combines two hot topics (particle filters and random sets) into a delicious feast of algorithms, Monte Carlo simulations, important applications, and mathematics that is new for normal engineers, all served up with clarity and thoroughness. The seminal research on random sets applied to tracking and data fusion problems is due to Ron Mahler [1]. It is clearly a good idea to apply random sets to such problems; in fact, it is hard to avoid using them either implicitly or explicitly. For example, the set of hypotheses for the well-known multiple hypothesis tracking (MHT) algorithms are random sets. The set of hypotheses for MHT is obviously random, because it depends on the measurements (which are random owing to noise errors and random target states). This is exactly the viewpoint adopted in [5], in which one is forced to compute equivalence classes of random sets in order to obtain a set of hypotheses that is mutually exclusive and collectively exhaustive, as required for any rational probability model of the situation (Bayesian or otherwise). It turns out that “fuzzy sets” correspond to equivalence classes of random sets, as explained by Goodman [6]. The set of particles in particle filters is also random, because it is generated from random numbers, but almost no papers explicitly use non-trivial random set theory for particle filter design. In fact, there are very few papers that explicitly talk about random sets and particle filters together, with the conspicuous exception of [20]. Nevertheless, random sets are a mature and well documented mathematical tool, as shown in [1], [2], [3], and [4]. You should not be afraid of random set mathematics; Ron Mahler has written a series of highly accessible tutorials on using random finite sets which avoid the pathologies and complexity of random unfettered sets (e.g., [26] and [28]).

Comparative investigation into Viterbi based and multiple hypothesis based track stitching

In this study a comparative analysis is performed between a novel Viterbi based and multiple hypothesis based track stitching algorithms. The track fragments in the Viterbi based track stitching algorithm are modelled as nodes in a trellis structure. A sequential Viterbi data association algorithm is then used to solve the trellis and associate track fragments with each other. A Kalman filter is used to determine the possible associations as well as the probabilities of the associations between the track fragments. In the multiple hypothesis track stitching algorithm, the hypothesis based multiple hypothesis tracking (MHT) algorithm is extended to perform track fragment to track fragment associations, rather than associating observations to tracks. Aspects of the developed multiple hypothesis algorithm are compared with implementations of a similar nature. Novel aspects of this research include the modification of the sequential Viterbi algorithm, as well as the extension of the MHT algorithm to solve the track stitching problem. It was found that the sequential Viterbi track stitching algorithm performed somewhat better than the multiple hypothesis track stitching algorithm for similar execution times. The Viterbi based track stitching algorithm is also shown to produce more consistently acceptable results.

Bayesian Multiple-Hypothesis Tracking of Merging and Splitting Targets

This paper presents a Bayesian model for the multiple-target tracking problem that handles a varying number of splitting and merging targets applied to convective cloud tracking. The model decomposes the tracking solution into events and target states. The events include target births, deaths, splits, and merges. The target states contain both the target positions and attributes. By updating the target attributes and conditioning the events on their updated values, we can include high-level domain knowledge into the system. This strategy improves the tracking accuracy and the computational efficiency since we focus only on likely events for each situation. A two-step multiple-hypothesis tracking algorithm has been developed to estimate the model state. The proposed approach is tested by both simulation and real data for mesoscale convective system tracking.

Multitarget Tracking with Doppler Ambiguity

A new approach for multitarget detection and tracking with Doppler ambiguity is presented. Ambiguous Doppler measurements, in addition to the position measurements, are directly used in data association and tracking. Based on the unscented Kalman filter (UKF), the multiple hypothesis tracking (MHT) algorithm and the probabilistic data association (PDA) algorithm, three different methods for solving the ambiguity, independent of the choice of particular pulse repetition frequency (PRF) values, in the tracking level are proposed. First, the UKF is modified to handle explicitly the ambiguous Doppler measurement. It is shown that the modified UKF can achieve better tracking performance than the standard UKF. On the other hand, the MHT and PDA algorithms, both of which are usually used to solve the measurement-to-track association problem, are modified here to handle the Doppler ambiguity problem. Simulations are performed to demonstrate the effectiveness of the new algorithms.

Multiple Vehicle-like Target Tracking Based on the Velodyne LiDAR

This paper proposes a novel multiple vehicle-like target tracking method based on a Velodyne HDL64E light detection and ranging (LiDAR) system. The proposed method combines multiple hypothesis tracking (MHT) algorithm with dynamic point cloud registration (DPCR), which is able to solve the multiple vehicle-like target tracking in highly dynamic urban environments without any auxiliary information from GPS or IMU. Specifically, to track targets consistently, the DPCR is developed to calculate accurately the pose of the ego-vehicle for the transformation of raw measurements taken in the moving coordinate systems into a static absolute coordinate system; while in turn, MHT helps to improve the performance of DPCR by discriminating and removing the dynamic points from the scene. Furthermore, the proposed MHT method is also able to solve the occlusion problem existing in the point cloud. Experiments on sets of urban environments prove that the presented method is effective and robust, even in highly dynamic environments.

Robust fluoroscopic tracking of fiducial markers: exploiting the spatial constraints

Two new fluoroscopic fiducial tracking methods that exploit the spatial relationship among the multiple implanted fiducial to achieve fast, accurate and robust tracking are proposed in this paper. The spatial relationship between multiple implanted markers are modeled as Gaussian distributions of their pairwise distances over time. The means and standard deviations of these distances are learned from training sequences, and pairwise distances that deviate from these learned distributions are assigned a low spatial matching score. The spatial constraints are incorporated in two different algorithms: a stochastic tracking method and a detection based method. In the stochastic method, hypotheses of the ‘true’ fiducial position are sampled from a pre-trained respiration motion model. Each hypothesis is assigned an importance value based on image matching score and spatial matching score. Learning the parameters of the motion model is needed in addition to learning the distribution parameters of the pairwise distances in the proposed stochastic tracking approach. In the detection based method, a set of possible marker locations are identified by using a template matching based fiducial detector. The best location is obtained by optimizing the image matching score and spatial matching score through non-serial dynamic programming. In this detection based approach, there is no need to learn the respiration motion model. The two proposed algorithms are compared with a recent work using a multiple hypothesis tracking (MHT) algorithm which is denoted by MHT, Tang et al (2007 Phys. Med. Biol. 52 4081–98). Phantom experiments were performed using fluoroscopic videos captured with known motion relative to an anthropomorphic phantom. The patient experiments were performed using a retrospective study of 16 fluoroscopic videos of liver cancer patients with implanted fiducials. For the motion phantom data sets, the detection based approach has the smallest tracking error (μerr: 0.78–1.74 mm, σerr: 0.39–1.16 mm) for the images taken at low exposure (50 mAs). At higher exposure (500 mAs), the stochastic method gave the best performance (μerr: ∼0.39 mm, σerr: ∼0.27 mm). In contrast, the tracker (MHT) that does not model the spatial constraints only performs well when there is no occluded fiducial. With the RANDO phantom data, both of our proposed methods performed well and have the mean tracking errors around ∼1.8 mm with the standard deviations ∼0.93 mm at 100 mAs and ∼0.91 mm with 0.88 mm standard deviation at 500 mAs. The MHT tracker has the largest tracking errors with mean ∼4.8 mm) and standard deviation ∼2.4 mm in both sessions with the Rondo phantom data. On the patient data sets, the detection based method gave the smallest error (μerr: 0.39 mm, σerr: ∼0.19 mm). The stochastic method performed well (μerr: ∼0.58 mm, σerr: ∼0.39 mm) when the patient breathed consistently, the accuracy dropped to (μerr: ∼1.55 mm) when the patient breathed differently across sessions.

Deinterleaving pulse trains in unconventional circumstances using multiple hypothesis tracking algorithm

The main function of Electronic Support Measure (ESM) is to receive, measure and deinterleave pulses, and then identify alternative threat emitters. Among these processes, pulse deinterleaving is vitally important because dense electromagnetic environments could cause an ESM system to receive a seemingly random pulse stream consisting of interleaved pulse trains with high noise levels. Only when we segregate different radar pulse trains from the pulse stream can we proceed with further processing. Traditional deinterleaving algorithms have demonstrated instability in unconventional circumstances (such as agility of pulse repetition interval (PRI), large noise and jitter, missing of intercepted pulses). Based on the dynamic process of different emitters, a new Statistical Association Pulse Deinterleaving (SAPD) approach is proposed based on the Multiple Hypothesis Tracking (MHT) algorithm in Multiple Target Tracking system. Simulation results have shown that the proposed algorithm can successfully identify pulse trains with constant, jittered and staggered PRI, and provide much greater accuracy in PRI estimation and pulse classification than traditional algorithms, with the presence of large noise, frequency jitter, and many missing pulses.

Automated 5-D analysis of cell migration and interaction in the thymic cortex from time-lapse sequences of 3-D multi-channel multi-photon images

This paper presents automated methods to quantify dynamic phenomena such as cell–cell interactions and cell migration patterns from time-lapse series of multi-channel three-dimensional image stacks of living specimens. Various 5-dimensional (x, y, z, t, λ) images containing dendritic cells (DC), and T-cells or thymocytes in the developing mouse thymic cortex and lymph node were acquired by two-photon laser scanning microscopy (TPLSM). The cells were delineated automatically using a mean-shift clustering algorithm. This enables morphological measurements to be computed. A robust multiple-hypothesis tracking algorithm was used to track thymocytes (the DC were stationary). The tracking data enable dynamic measurements to be computed, including migratory patterns of thymocytes, and duration of thymocyte-DC contacts. Software was developed for efficient inspection, corrective editing, and validation of the automated analysis results. Our software-generated results agreed with manually generated measurements to within 8%.