Correlation-Based Tracking of Multiple Targets With Hierarchical Layered Structure.

The problem of distributed tracking of multiple targets is tackled by exploiting sensor mobility and the presence of sparsity in the sensor data covariance matrix. Sparse matrix decomposition relying on norm-one/two regularization is integrated with a kinematic framework to identify informative sensors, associate them with the targets, and enable them to follow closely the moving targets. Coordinate descent techniques are employed to determine in a distributed way the target-informative sensors, while the modified barrier method is employed to minimize proper error covariance matrices acquired by extended Kalman filtering. Different from existing approaches which force all sensors to move, here, local updating recursive rules are obtained only for the target-informative sensors that can update their location and follow closely the corresponding targets while staying connected. Simulations advocate that the proposed scheme outperforms alternative tracking schemes while accurately tracks multiple targets by imposing mobility only on the target-informative sensors.

In this paper, we present a new approach for online joint detection and tracking for multiple targets. We combine a deterministic clustering algorithm for target detection with a sequential Monte Carlo method for multiple target tracking. The proposed approach continuously monitors the appearance and disappearance of a set of regions of interest for target detection within the surveillance region. No computational effort for target tracking is expended unless these regions of interest are persistently detected. In addition, we also integrate a very efficient 2D data assignment algorithm into the sampling method for the data association problem. The proposed approach is applicable to nonlinear and nonGaussian models for the target dynamics and measurement likelihood. Computer simulations demonstrate that the proposed hybrid approach is robust in performing joint detection and tracking for multiple targets even though the environment is hostile in terms of high clutter density and low target detection probability

This paper presents an energy-aware, sleep scheduling algorithm called SSMTT to support multiple target tracking sensor networks. SSMTT leverages the awakening result of interfering targets to save the energy consumption on proactive wake-up communication. For the alarm message-miss problem introduced by multiple target tracking, we present a solution that involves scheduling the sensor nodes' sleep pattern. We compare SSMTT against three sleep scheduling algorithms for single target tracking: the legacy circle scheme, MCTA, and TDSS. Our experimental evaluations show that SSMTT achieves better energy efficiency than handling multiple targets separately through single target tracking algorithms.

Kalman filter and its families have played an important role in information gathering, such as target tracking. Data association techniques have also been developed to allow the Kalman filter to track multiple targets simultaneously. This paper revisits the principle and applications of the Kalman filter for single target tracking and multiple hypothesis tracking (MHT) for multiple target tracking. We present the brief review of the Bayes filter family and introduce a brief derivation of the Kalman filter and MHT. We show examples for both single and multiple targets tracking in simulation to illustrate the efficacy of Kalman filter-based algorithms in target tracking scenarios.

A novel particle filter for multiple target tracking with track-before-detect measurement models is proposed. Particle filters efficiently perform target tracking under nonlinear or non-Gaussian models. However, their application to multiple target tracking suffers from the curse of dimensionality. We introduce an efficient particle filter for multiple target tracking which deals with the curse of dimensionality better than previously developed methods. The proposed algorithm is tested and compared to other multiple target tracking particle filters.

This paper evaluates the performance of multiple target tracking (MTT) algorithms in real-life stressful radar tracking environments. Real closely spaced maneuver radar data, generated by six F-18 fighters and other targets, were collected jointly by the defence departments of Canada and United States to support this practical MTT algorithm evaluation study. A set of performance metrics was defined here to compare the suboptimal nearest neighbor (SNN), global nearest neighbor (GNN), and various variants of the joint probabilistic data association (JPDA) MTT trackers. Results reveal an interesting result that all these MTT algorithms exhibited very close performance. In addition, the weighted sum approach of the PDA/JPDA trackers which are theoretically effective were observed to perform poorly in tracking closely spaced targets. Overall speaking, the GNN filter based on the Munkres algorithm had the best performance in terms of both tracking performance and robustness.

Tracking of multiple extended three-dimensional targets in the presence of partial occlusions from point cloud measurement sets remains a challenge but has obvious applications in autonomous vehicles. We demonstrate that existing range-image based approaches to point cloud segmentation and motion detection can be fused with fast point cloud registration techniques to aid track association. Consequently, multiple generic extended targets in point clouds can be tracked. We also show that robust occlusion handling can be achieved using temporally accumulated geometric information from co-registered point clouds in a static local reference frame. The occlusion logic is based on a novel probabilistic occlusion reasoning approach combined with a simulated geometric handling of self-occlusions. The proposed tracking pipeline was tested on synthetic target geometries superimposed on real-world backgrounds, as well as against measured data from benchmark autonomous driving datasets. The targets tracked encountered both artificially introduced and naturally occurring foreground occlusions as well as self-occlusions. Results show notable robustness to foreground occlusions. Incorrect track associations are possible when multiple targets in proximity display similar dynamics and possess nearly identical geometries. Our sequence of tracking algorithms relies on multiple tunable hyper-parameters. These parameters require further automated runtime-optimization before robust, real-time field application can be achieved.

This paper describes the use of a simulated real time system of a feed-forward neural network, a recurrent neural network and a set of expert rules in solving the problem of multiple target tracking. It is assumed that data is provided in the from of blips, taken off 3 consecutive focal plane arrays, operating at visible or infrared wavelengths. In this paper, the task of multiple target tracking is transformed from one of blip-frame data association to one of target clustering, which in turn is broken down and solved in four stages. Each stage is described and mapped with the use of a feed-forward, a recurrent neural network or a set of fuzzy rules. The first and second stages of the solution procedure involve the use of two feed-forward neural network modules, while the third and forth stages use a recurrent neural network module and a set of expert rules module. The multiple target tracking solution procedure is simulated through use of FORTRAN code. In principle the number of targets that can be tracked with the routine is unlimited. However, in reality, the number of targets is dictated by the number of neurons, which in turn is constrained by hardware requirements. Software simulation results shows that the multiple target tracking code is capable of tracking an arbitrary number of targets very efficiently. The program was tested and debugged for use in the tracking of sets of multiple targets; ranging from 2 to 14. Results indicated that once the average acceleration of the targets is adequately evaluated, track files could be developed with 100% accuracy.

The related theories and technologies on sensor networks are a hot research field. Due to great uncertainties existed in its data processing, multiple target tracking (MTT) has become a difficult problem in distributed sensor networks (DNWs). Hence, this paper mainly studies the uncertainty problems on MTT in the framework of DNW systems by using the fuzzy theory. According to its model structure, a DNW system can be divided into three layers according to its model structure: the data capture layer, data processing layer and data analysis layer. Based on data processing in different layers, the MTT process can be classified as three phrases: data association/fusion of measurementmeasurement/sensor track, sensor track-local track, and local track-global track. It presents a complete procedure of data processing on MTT in a real tracking system. Then, the uncertainty problems on MTT are classified. After analyzing data characteristics, one can utilize the fuzzy information processing technology to solve these problems. Finally, the difficulties on MTT in real tracking systems are summarized.

Implementing and Analyzing a Recursive Technique for Building Path Oblivious RAM

State-of-the-art multi-object tracking (MOT) methods follow the tracking-by-detection paradigm, where object trajectories are obtained by associating per-frame outputs of object detectors. In crowded scenes, however, detectors often fail to obtain accurate detections due to heavy occlusions and high crowd density. In this paper, we propose a new MOT paradigm, tracking-by-counting, tailored for crowded scenes. Using crowd density maps, we jointly model detection, counting, and tracking of multiple targets as a network flow program, which simultaneously finds the global optimal detections and trajectories of multiple targets over the whole video. This is in contrast to prior MOT methods that either ignore the crowd density and thus are prone to errors in crowded scenes, or rely on a suboptimal two-step process using heuristic density-aware point-tracks for matching targets. Our approach yields promising results on public benchmarks of various domains including people tracking, cell tracking, and fish tracking.

Reliable track continuity is an important characteristic of multiple target tracking (MTT) algorithms. In the specific case of tracking multiple ground targets from an aerial platform, challenges arise due to realistic operating environments such as imperfections in the measurement source. Some popular visual detection techniques include Kanade-Lucas-Tomasi (KLT)-based motion detection, difference imaging, and object feature matching. Each of these algorithmic detectors has fundamental limitations in regard to providing consistent measurements. In this paper we present a scalable detection framework that leverages multiple measurement sources. We also present the recursive random sample consensus (R-RANSAC) algorithm in a data fusion architecture that can simultaneously accommodate multiple measurement sources. We demonstrate robust track continuity using post-processed flight data and show real-time computational performance.

Data association plays an important role in multiple target tracking. Target's polarization information can't update target's track, but it can discriminate different targets and clutter effectively. Therefore, the non-determinacy of association is reduced. Given the target's polarization information, the generalized "nearest neighbor" algorithm is proposed according to the extended distance defined in the space-polarization joint domains. The simulation verifies that the "generalized nearest neighbor" outperforms the conventional "nearest neighbor"

Multiple target tracking (MTT) has received much attention recently for various applications in the military as well as the Strategic Defense Initiative areas. Data association is one of the critical computation in MTT problems, because erroneous data associations often result in lost tracks. The joint probabilistic data association (JPDA) algorithm is a good approach to solve the data association problem. However, the computation complexity of this algorithm increases rapidly with the number of targets and radar returns. Neural networks have been considered to approximate the JPDA and ease the computation burden through the parallel processing. In this paper, we propose a neural network data association (NNDA) algorithm for the solution of the data association problems. Simulation results show the following three controversial issues: first, NNDA can track multiple targets with performance compatible with JPDA. Second, when the prediction filter can not provide an enough accurate prediction data ( This sometimes occurs when the prediction model can not match the tracking environment precisely enough), the neural computation provides better performace than JPDA. Third, the performance of NNDA is not affected by the number while that of JPDA degrades with the increase of target number. As a whole, this paper presents a neural network which not only possesses the intrinsic ability of parallel computation but also provides conditionally better tracking performance than JPDA.

Aim to solve the tracking of multiple group targets in strong clutter environment, a rectangular box is used to deal with extension state of group targets. Combining the box particle filter algorithm and the cardinalized probability hypothesis density (CPHD) algorithm, a CPHD algorithm for multiple group targets tracking based on box particle filter (BPF), which is called BPF-CPHD, is proposed in this paper. Compared with the existed algorithm of multiple group targets tracking, the proposed algorithm can improve the tracking accuracy and efficiency greatly. Simulation results confirm the effectiveness of the BPF-CPHD.