Multi-Level Cooperative Fusion of GM-PHD Filters for Online Multiple Human Tracking

Fu, Zeyu; Angelini, Federico; Chambers, Jonathon A.; Naqvi, Syed Mohsen

doi:10.1109/tmm.2019.2902480

Cited by 82 publications

(36 citation statements)

References 55 publications

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“…The proposed method is still comparative and positioned at meaningful spot for realtime application as shown in Figure 5-(b). In addition to our tracking algorithm (GMPHD-OGM), many PHD filter based online approaches [16], [18], [22], [38], [41]- [43] have been proposed in the past decade. Against them, GMPHD-OGM achieves not only the best MOTA, MOTP, MT, ML, FN, and speed scores on MOT15 but also the second best MOTA, speed, and best MT, FN, and Frag scores in MOT17.…”

Section: B Evaluation Resultsmentioning

confidence: 99%

“…Recently, the closed-form implementations [2], [3] of the probability hypothesis density (PHD) filtering have been employed as an emerging theory for many online MOT methods [16]- [18], [22], [38], [41]- [43]. That is because Vo et al [2], [3] provided not only theoretically optimal approach to the online multi-target Bayes filtering but also approximate the original PHD recursions involving multiple integrals, which alleviate the computational intractability.…”

mentioning

confidence: 99%

“…They make the GMPHD filter be sophisticated and fit to video based MOT. Besides, various tracking methods [22], [38], [41] utilizing the PHD filters have been proposed.…”

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confidence: 99%

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Online Multi-Object Tracking With GMPHD Filter and Occlusion Group Management

Song

Yoon

et al. 2019

IEEE Access

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In this paper, we propose an efficient online multiobject tracking framework based on the Gaussian mixture probability hypothesis density (GMPHD) filter and occlusion group management scheme where the GMPHD filter utilizes hierarchical data association to reduce the false negatives caused by miss detection. The hierarchical data association consists of two steps: detection-to-track and track-to-track associations, which can recover the lost tracks and their switched IDs. In addition, the proposed framework is equipped with an object grouping management scheme which handles occlusion problems with two main parts. The first part is "track merging" which can merge the false positive tracks caused by false positive detections from occlusions, where the false positive tracks are usually occluded with a measure. The measure is the occlusion ratio between visual objects, sum-of-intersection-over-area (SIOA) we defined instead of intersection-over-union (IOU) metric. The second part is "occlusion group energy minimization (OGEM)" which prevents the occluded true positive tracks from false "track merging". We define each group of the occluded objects as an energy function and find an optimal hypothesis which makes the energy minimal. We evaluate the proposed tracker in benchmark datasets such as MOT15 and MOT17 which are built for multi-person tracking. An ablation study in training dataset shows that not only "track merging" and "OGEM" complement each other but also the proposed tracking method has more robust performance and less sensitive to parameters than baseline methods. Also, SIOA works better than IOU for various sizes of false positives. Experimental results show that the proposed tracker efficiently handles occlusion situations and achieves competitive performance compared to the state-of-theart methods. Especially, our method shows the best multi-object tracking accuracy among the online and real-time executable methods.

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Section: B Evaluation Resultsmentioning

confidence: 99%

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confidence: 99%

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Online Multi-Object Tracking With GMPHD Filter and Occlusion Group Management

Song

Yoon

et al. 2019

IEEE Access

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“…Random-finite-set-based approaches have also been studied, as they are Bayesian filterings for multiple object tracking that can operate in real time without any special modifications. In paticular, some GMPHD-based approaches have been proposed [19]- [22]. However, GMPHDbased approaches suffer from many ID switches, as they track probability density instead of individual objects.…”

Section: B Related Studies In Computer Visionmentioning

confidence: 99%

Exclusive Association Sampling to Improve Bayesian Multi-Target Tracking

Taguchi

Kidono

2020

IEEE Access

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Multi-target tracking has been studied for many years, yet it remains a challenging problem, particularly in terms of implementing data association when tracking targets over several time steps. To achieve robustness, probabilistic approaches have been proposed, including Bayesian multi-target tracking methods. However, these approaches involve high calculation costs, which are incompatible with real-time applications. We propose exclusive association sampling (EAS) to improve the efficiency of Bayesian multi-target tracking methods. Although EAS is a simple procedure composed of random sorting and sampling based on the observation probability, it can be employed to increase the efficiency of association candidate generation and the calculation of statistical values. In this study, we proposed two Bayesian multitarget tracking methods based on EAS: stochastic joint probabilistic data association (SJPDA) and Rao-Blackwellized particle filter with EAS (RBPF with EAS). Evaluation of these methods with simulated data shows that integrating EAS into these methods can enhance their speed and accuracy. Moreover, evaluation on open datasets used for pedestrian tracking on camera sequences shows that the proposed methods achieve significantly better performance on some important metrics compared with representative methods. INDEX TERMS multi-target tracking, joint probabilistic data association, Rao-Blackwellized particle filter

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“…The high computational complexity of the PHD filter and multidimensional integration problems with indefinite dimensions can create difficulties in practical engineering applications. Vo et al proposed a GM-PHD filter based on a linear Gaussian system to address this issue [32][33][34]. The filter is based on certain assumptions that are summarized below.…”

Section: Traditional Gm-phd Filter (A) Assumptionsmentioning

confidence: 99%

An Improved UAV-PHD Filter-Based Trajectory Tracking Algorithm for Multi-UAVs in Future 5G IoT Scenarios

Tang

Hong

et al. 2019

Electronics

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The 5G cellular network is expected to provide core service platform for the expanded Internet of Things (IoT) by supporting enhanced mobile broadband (eMBB), massive machine-type communication (mMTC), and ultra-reliable low latency communications (URLLC). Unmanned aerial vehicles (UAVs), also known as drones, provide civil, commercial, and government services in various fields. Particularly in a 5G IoT scenario, UAV-aided network communications will fulfill an increasingly important role and will require the tracking of multiple UAV targets. As UAVs move quickly, maintaining the stability of the communication connection in 5G will be a challenge. Therefore, it is necessary to track the trajectory of UAVs. At present, the GM-PHD filter has a problem that the new target intensity must be known, and it cannot obtain the moving target trajectory and the influence of the clutter is likely to cause false alarm. A UAV-PHD filter is proposed in this work to improve the traditional GM-PHD filter by applying machine learning to the emergency detection and trajectory tracking of UAV targets. An out-of-sight detection algorithm for multiple UAVs is then presented to improve tracking performance. The method is assessed by simulation using MATLAB, and OSPA distance is utilized as an evaluation indicator. The simulation results illustrate that the proposed method can be applied to the tracking of multiple UAV targets in future 5G-IoT scenarios, and the performance is superior to the traditional GM-PHD filter.

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Multi-Level Cooperative Fusion of GM-PHD Filters for Online Multiple Human Tracking

Cited by 82 publications

References 55 publications

Online Multi-Object Tracking With GMPHD Filter and Occlusion Group Management

Online Multi-Object Tracking With GMPHD Filter and Occlusion Group Management

Exclusive Association Sampling to Improve Bayesian Multi-Target Tracking

An Improved UAV-PHD Filter-Based Trajectory Tracking Algorithm for Multi-UAVs in Future 5G IoT Scenarios

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