Data Association Algorithm Research Articles

Augmented input estimation in multiple maneuvering target tracking

This paper presents augmented input estimation (AIE) for multiple maneuvering target tracking. Multi-target tracking (MTT) is based on two main parts, data association and estimation. In data association (DA), the best observations are assigned to the considered tracks. In real conditions, the number of observations is more than targets and also locations of observations are often so scattered that the association between targets and observations cannot be done simply. In this case, for general MTT problems with unknown numbers of targets, we present a Markov chain MonteCarlo DA (MCMCDA) algorithm that approximates the optimal Bayesian filter with low complexity in computations. After DA, estimation and tracking should be done. Since in general cases, many targets can have maneuvering motions, then AIE is proposed to cover both the non-maneuvering and maneuvering parts of motion and the maneuver detection procedure is eliminated. This model with an input estimation (IE) approach is a special augmentation in the state space model which considers both the state vector and the unknown input vector as a new augmented state vector. Some comparisons based on the Monte-Carlo simulations are also made to evaluate the performances of the proposed method and other older methods in MTT.

Markov Chain Realization of Multiple Detection Joint Integrated Probabilistic Data Association.

In multiple detection target tracking environments, PDA-based algorithms such as multiple detection joint integrated probabilistic data association (MD-JIPDA) utilize the measurement partition method to generate measurement cells. Thus, one-to-many track-to-measurements associations can be realized. However, in this structure, the number of joint data association events grows exponentially with the number of measurement cells and the number of tracks. MD-JIPDA is plagued by large increases in computational complexity when targets are closely spaced or move cross each other, especially in multiple detection scenarios. Here, the multiple detection Markov chain joint integrated probabilistic data association (MD-MC-JIPDA) is proposed, in which a Markov chain is used to generate random data association sequences. These sequences are substitutes for the association events. The Markov chain process significantly reduces the computational cost since only a few association sequences are generated while keeping preferable tracking performance. Finally, MD-MC-JIPDA is experimentally validated to demonstrate its effectiveness compared with some of the existing multiple detection data association algorithms.

Data association for extended target tracking by BP

Data association with great efficiency is an important problem in multiple extended target tracking. It is more challenging than that in point target tracking as the measurements of extended targets are more. This study presents a graphical model formulation of data association for extended target tracking, which is solved by belief propagation (BP) to obtain estimations. To construct the graphical model, a simplified scheme for measurements is proposed to improve the tracking efficiency, which is based on minimal spanning tree algorithm. Then, the data association problem according to the simplified measurement set is presented, solved by BP. Finally, experiment results show the proposed algorithm has advantages over the algorithm based on joint probabilistic data association and the previous data association algorithm, introduced by Vivone and Braca, in terms of robustness and efficiency.

Joint integrated track splitting for multi-path multi-target tracking using OTHR detections

In target tracking applications where an over-the-horizon radar (OTHR) is used to gather measurements, one target may generate more than one measurement at each scan due to the multi-path propagation effect. However, traditional tracking methods obtain the data association probabilities based on track-to-measurement data association events under the assumption that each target can generate at most one detection at each scan, leading to poor performances if these methods are applied to multiple detection applications such as OTHR applications. In this paper, we develop a multi-path multi-target tracking algorithm entitled multiple detection joint integrated track splitting (MD-JITS). This novel algorithm jointly solves the measurement origin uncertainty and measurement path model uncertainty. The probability of target existence (PTE) is utilized in the OTHR application as a track quality measure for true track confirmation and false track discrimination. The data association algorithm of MD-JITS the proposed algorithm is realized based on measurement cells that each measurement cell consists of one or more of the validated measurements, while considering the measurement path model. The proposed algorithm is compared with the multiple detection joint integrated probabilistic data association (MD-JIPDA) algorithm in a multi-target crossing scenario, implementing the OTHR system in the presence of clutter and failed target detections, to demonstrate the desired effectiveness.

Research on Moving Target Tracking Algorithm Based on Lidar and Visual Fusion

Multi-sensor fusion and target tracking are two key technologies for the environmental awareness system of autonomous vehicles. In this paper, a moving target tracking method based on the fusion of Lidar and binocular camera is proposed. Firstly, the position information obtained by the two types of sensors is fused at decision level by using adaptive weighting algorithm, and then the Joint Probability Data Association (JPDA) algorithm is correlated with the result of fusion to achieve multi-target tracking. Tested at a curve in the campus and compared with the Extended Kalman Filter (EKF) algorithm, the experimental results show that this algorithm can effectively overcome the limitation of a single sensor and track more accurately.

Linear multitarget integrated probabilistic data association for multiple detection target tracking

The point target assumption, which suggests that a target can generate at most one measurement at a time, is used in typical target tracking algorithms. However, in many practical applications, multiple scattering points of a target can be resolved using a high-resolution sensor, which gives rise to the multiple detection problem. The typical algorithms with the point target assumption are not eligible for multiple detection tracking environments. The multiple detection joint integrated probabilistic data association algorithm is designed to solve the multiple detection multitarget tracking problem. However, the computational complexity of this algorithm grows exponentially with the number of tracks and measurement cells. Here, multiple detection linear multitarget integrated probabilistic data association is proposed to enhance computational efficiency by introducing the modulated clutter measurement density, which takes into account the contributions of clutter as well as other targets of each measurement cell. The computational complexity of the proposed algorithm is linear in the number of tracks and the number of measurement cells. Simulation results verify the applicability and efficiency of the proposed algorithm in multiple detection multitarget tracking scenarios.

A Method to Track Targets in Three-Dimensional Space Using an Imaging Sonar.

This paper introduces a methodology applying an imaging sonar for three-dimensional (3D) target tracking underwater. The key process in this work involves obtaining the target’s position in space using two images of the same scene, acquired by an adaptive resolution imaging sonar (ARIS) at different positions. A data association algorithm was designed to connect the same target in image sequences. The goal of this work was to track multiple targets in 3D space. The ARIS provides sequences of bi-dimensional images from the backscattered energy according to the range and azimuth. The challenge involved determining the missing elevation information for the observed object within the sonar detection range. By computing the geometrical transformation between the acquisition planar images and the cubical space, using only the sonar information that included the posture and moving speed of the ARIS, the target’s elevation information was obtained. To evaluate the performance of the proposed method, an indoor experiment was conducted using the ARIS. On the basis of the experimental results, we confirmed that the proposed method effectively obtained the target’s position in 3D space. A moving target simulation was also conducted, and the results showed that this method was effective for moving targets. Finally, a field experiment was performed to obtain the vertical distribution and track the 3D trajectories of fish.

Automotive radar system for multiple‐vehicle detection and tracking in urban environments

In this study, an enhanced approach for automotive radar systems is proposed to solve the detection, tracking, and track management problem in the presence of clutter with high accuracy and low computational cost. The unscented Kalman filter (UKF) with a constant turn rate and acceleration (CTRA) dynamic model is employed for target tracking, and the tracking accuracy is enhanced by incorporating the linear regression (LR) algorithm into the UKF-CTRA algorithm. We investigate, for the first time, the Joint Probabilistic Data Association (JPDA) algorithm for data association, and the composite M/N tests for track management. The capability of the proposed approach (CTRA-UKF-LR-JPDA-composite-M/N-tests) is demonstrated by comparing it with various algorithms for different single and multi-target tracking scenarios and for various sets of parameter regimes. The results show the superior performance of the proposed method over other existing techniques in automotive radar systems. This reveals the effectiveness of the proposed algorithm as a promising technique in automotive applications.

Structural Constraint Data Association for Online Multi-object Tracking

Online two-dimensional (2D) multi-object tracking (MOT) is a challenging task when the objects of interest have similar appearances. In that case, the motion of objects is another helpful cue for tracking and discriminating multiple objects. However, when using a single moving camera for online 2D MOT, observable motion cues are contaminated by global camera movements and, thus, are not always predictable. To deal with unexpected camera motion, we propose a new data association method that effectively exploits structural constraints in the presence of large camera motion. In addition, to reduce incorrect associations with mis-detections and false positives, we develop a novel event aggregation method to integrate assignment costs computed by structural constraints. We also utilize structural constraints to track missing objects when they are re-detected again. By doing this, identities of the missing objects can be retained continuously. Experimental results validated the effectiveness of the proposed data association algorithm under unexpected camera motions. In addition, tracking results on a large number of benchmark datasets demonstrated that the proposed MOT algorithm performs robustly and favorably against various online methods in terms of several quantitative metrics, and that its performance is comparable to offline methods.

Rough fuzzy joint probabilistic association for tracking multiple targets in the presence of ECM

A novel rough fuzzy joint probabilistic data association algorithm (RF-JPDA) is presented to improve the performance of multitarget tracking in the presence of clutter, electronic countermeasures (ECM) and false alarms. The possibility data association matrix is evaluated by applying upper and lower approximations of validated measurements which are obtained from the radar. Four case studies are taken to validate the proposed data association algorithm. The proposed technique performance has been compared with conventional joint probabilistic data association filter (JPDA), fuzzy clustering means (FCM), and fuzzy Genetic Algorithm (Fuzzy-GA) approaches. A hybrid data association approach is formulated and examined for multi-target tracking using intelligent technique. Further, it is evident from the experimental results that RF-JPDA approach is providing enhanced performance in terms of position root mean square error (RMSE), velocity RMSE and execution time for all cases. The average position and velocity RMSE of RF-JPDA are 42.3% and 16.98% less when compared to conventional JPDA. Thus accomplishing novel and effective multiple target tracking algorithm based on expert systems.

Efficient data association to targets for tracking in passive wireless sensor networks

We have developed an algorithm for efficient data association to targets, the two-way cluster association (TWCA) algorithm, for tracking multiple targets in passive wireless sensor networks (PWSNs). PWSN applications require that the sensors have low computational and communication loads as the sensors are battery powered. We choose PWSNs as each sensor node triggers target detection and tracking only in the presence of the signals. However, the PWSNs make the association difficult because the detected signal by a passive sensor may come from targets nearby and/or far from the sensor depending on the target signal powers. The difficulty for the data association is further amplified when multiple targets undergo complex maneuvers including merging and split. The TWCA algorithm solves the association problem with very simple operations by using the clusters of the detecting sensors aggregated around the targets. The TWCA is a significant improvement over the previous studies including our previous rule based cluster association (RBCA) which works only for targets in linear motions and the number of targets being constant during tracking. TWCA can track unknown number of targets in a wide range of non-linear maneuvers with very low computation load and high track accuracy as demonstrated by our simulation.

Improved image processing-based crop detection using Kalman filtering and the Hungarian algorithm

There is increasing interest in the use of image processing techniques for crop detection in intelligent weeding applications. An effective system for crop detection requires a high degree of adaptability to challenging circumstances such as different weather conditions and image capture conditions (vibration, variations in speed, etc.). To achieve the goal of a robust crop detection system, we have extended a previously-developed detection algorithm that is based on a combination of color-space and shape analysis, through the addition of object tracking. While the previous algorithm performed well in general, performance in sunny conditions was not as robust, opening up the possibility of improvement. The tracking algorithm consists of two steps. Firstly, we apply Kalman filtering to predict the new position of an object (a cauliflower plant in this case) in video sequences. Secondly, we use a data association algorithm (the Hungarian algorithm) to assign each detected crop that appears in each image to the correct crop trajectory. The recall matrix was used to evaluate the detection and tracking performance. With the help of tracking algorithm, detection failures were reduced, especially in sunny conditions, such that overall detection performance was raised from 97.28 to 99.3404%.

Study on multiple targets tracking algorithm based on multiple sensors

For the problem that traditional data association algorithms tend to coalesce neighboring tracks for multiple close targets tracking application in dense clutter, measurements adaptive censor (MAC) method to Set JPDA (SJPDA) algorithm was introduced in this paper, then the proposed the MACSJPDA algorithm of target tracking discards several data associations with small probability and accelerates the convergence speed of the SJPDA algorithm. The algorithm can achieve better effects of multiple targets tracking by multiple sensors in wireless sensor networks. Monte Carlo simulation revealed that estimation effect of the MACSJPDA algorithm is much smoother, and it needs less run time than SJPDA algorithm for handling closely spaced and crossing targets, in the meanwhile the mean optimal sub-pattern assignment (MOSPA) deviation of the MACSJPDA algorithm is also smaller.

Developing the fuzzy c-means clustering algorithm based on maximum entropy for multitarget tracking in a cluttered environment

For fast and more effective implementation of tracking multiple targets in a cluttered environment, we propose a multiple targets tracking (MTT) algorithm called maximum entropy fuzzy c-means clustering joint probabilistic data association that combines fuzzy c-means clustering and the joint probabilistic data association (PDA) algorithm. The algorithm uses the membership value to express the probability of the target originating from measurement. The membership value is obtained through fuzzy c-means clustering objective function optimized by the maximum entropy principle. When considering the effect of the public measurement, we use a correction factor to adjust the association probability matrix to estimate the state of the target. As this algorithm avoids confirmation matrix splitting, it can solve the high computational load problem of the joint PDA algorithm. The results of simulations and analysis conducted for tracking neighbor parallel targets and cross targets in a different density cluttered environment show that the proposed algorithm can realize MTT quickly and efficiently in a cluttered environment. Further, the performance of the proposed algorithm remains constant with increasing process noise variance. The proposed algorithm has the advantages of efficiency and low computational load, which can ensure optimum performance when tracking multiple targets in a dense cluttered environment.

Research and analysis of video image target tracking algorithm based on significance

A target tracking algorithm based on joint probabilistic data association encounters the problems of target loss and combinatorial explosion in cluttered and target-intensive tracking environments. In order to address these challenges, this paper proposes a video target tracking algorithm based on significance joint probabilistic data association. This algorithm detects the moving target in a video sequence through significance calculation. The detection results are classified, and the returned classes are then used as valid echoes for data association. Meanwhile, the validation matrix and joint probability in the joint probabilistic data association algorithm are redefined based on the significance information and a validation matrix and association probability based on significance is proposed. The association results are compensated and corrected using the colour association probability. Experiments in real scenes demonstrate that the proposed algorithm can suppress clutter in the background, simplify interconnected events, and solve the problem of computational combinatorial explosion.

Research and analysis of video image target tracking algorithm based on significance

A target tracking algorithm based on joint probabilistic data association encounters the problems of target loss and combinatorial explosion in cluttered and target-intensive tracking environments. In order to address these challenges, this paper proposes a video target tracking algorithm based on significance joint probabilistic data association. This algorithm detects the moving target in a video sequence through significance calculation. The detection results are classified, and the returned classes are then used as valid echoes for data association. Meanwhile, the validation matrix and joint probability in the joint probabilistic data association algorithm are redefined based on the significance information and a validation matrix and association probability based on significance is proposed. The association results are compensated and corrected using the colour association probability. Experiments in real scenes demonstrate that the proposed algorithm can suppress clutter in the background, simplify interconnected events, and solve the problem of computational combinatorial explosion.

Joint Probabilistic Data Association and Smoothing Applied to Multiple Space Object Tracking

A foundational aspect of space domain awareness is the ability to identify and track space objects, including space object discovery and custody. This paper demonstrates the power of combining an efficient multiple hypothesis joint probabilistic data association (MH-JPDA) algorithm with a fixed-interval smoother to simultaneously track multiple space objects. For newly discovered objects, statistical initial orbit determination (SIOD) is possible with a single short optical tracklet, but results in large initial uncertainties. Combining these uncertainties with closely spaced objects can result in highly ambiguous data associations, which can lead to poor state estimates and even filter divergence. This paper invokes MH-JPDA to probabilistically update multiple tracks with multiple simultaneous observations in a sequential filter, while avoiding assigning one-to-one associations. Once sufficient information has been collected, the space objects become uniquely distinguishable among each other. Subsequently, the smoother is applied to achieve improved association of the prior observations. MH-JPDA allows for immediate track formation (using SIOD) and sequential processing of incoming observations, providing statistically rigorous real-time state estimates, whereas smoothing produces a more-refined, higher-confidence overall track estimate at user-defined intervals. This paper demonstrates this approach within the Constrained Admissible Region, Multiple Hypothesis Filter (CAR-MHF) software by tracking a simulated break-up scenario.

Fuzzy logic approach to visual multi-object tracking

In this paper, a novel fuzzy logic data association algorithm is proposed for online visual multi-object tracking. Firstly, in the proposed algorithm, in order to incorporate expert experience into the data association for the improvement of performance in multi-object tracking, a fuzzy inference system based on knowledge is designed by using a set of fuzzy if-then rules. Given the error and change of error of motion, shape and appearance models in the last prediction, these rules are used to determine the fuzzy membership degrees that can be used to substitute the association probabilities between the objects and the measurements (or detection responses). Secondly, in order to deal with the fragmented trajectories caused by long-term occlusions, a track-to-track association approach based on the fuzzy synthetic function is proposed, which can effectively stitch track fragments (tracklets). Because of this, the proposed algorithm has the advantage that it does require no assumption of statistical models of measurement noise and of object dynamics. The experiment results on several public data sets show the efficiency and the ability to minimize the number of fragment tracks of the proposed algorithm.

Multi‐scan smoothing for tracking manoeuvering target trajectory in heavy cluttered environment

An automatic target tracking algorithm must be capable of dealing with an unknown number of targets and their trajectory behaviour inside the surveillance region. However, due to target motion uncertainties, heavily populated clutter measurements and low detection probabilities of targets, the smoothing algorithms often fail to detect the true number of target trajectories. In this study, the authors discussed some deficiencies and insignificances of existing smoothing algorithms and proposed a new smoothing data association based algorithm called fixed-interval integrated track splitting smoothing (ITS-S). The proposed algorithm employ smoothing data association algorithm and compared with existing smoothing algorithms outperform in terms of target trajectory accuracy and false-track discrimination (FTD). However, existing algorithms fail to generate smoothed target trajectory and provides insignificant FTD performance in such difficult environments as illustrated in this simulation study. The ITS-S shows improved smoothing performance compared with that of existing algorithms for a manoeuvering target tracking in a heavily populated cluttered environment and low detection probabilities.

Visual tracking based on edge field with object proposal association

In this paper, we present a novel tracking system based on edge-based object proposal and data association called object proposal association. Our object proposal method accurately detects and localizes objects in an image by searching for object-like regions, with the assumption that an object is represented by a closed boundary. To search for closed boundaries in an image, we present a new Edge Fields (EFs) technique. Using this technique, our method can extract high-quality edges and can obtain accurate boundaries from the image. The EFs technique consists of blurring and thresholding steps, where the former helps extract high-quality edges and the latter prevents the method from losing image details while blurring. After the method extracts object-like regions, we associate the regions in the previous frame with those in the current frame. For this purpose, using the Markov chain Monte Carlo data association (MCMCDA) algorithm, we can find pairs of similar regions across two frames. Experimental results demonstrate that our object proposal method is competitive with state-of-the-art object proposal methods on the PASCAL VOC 2007 dataset. Our tracking method is also competitive with state-of-the-art tracking methods on Object Tracking Benchmark dataset.