Development of a N-type GM-PHD filter for multiple target, multiple type visual tracking

Baisa, Nathanael L.; Wallace, Andrew M.

doi:10.1016/j.jvcir.2019.01.026

Cited by 40 publications

(27 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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.…”

mentioning

confidence: 99%

See 2 more Smart Citations

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

Song

Yoon

et al. 2019

IEEE Access

View full text Add to dashboard Cite

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.

show abstract

Section: B Evaluation Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

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

Song

Yoon

et al. 2019

IEEE Access

View full text Add to dashboard Cite

show abstract

“…mainly exploited by the signal processing community. GM-PHD and Sequential Monte Carlo (SMC)-PHD filters are two commonly used implementations in this theory, as they have been able to generate convincing tracking performance in video-based multi-target tracking [2], [3], [5], [7], [15]- [17]. This is attributed to the advantages of PHD filtering methods, as they have the ability to deal with varying number of targets, and also provide the estimates in both cardinality and localization with relatively low computational cost [2].…”

Section: Introductionmentioning

confidence: 99%

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

Angelini

Chambers

et al. 2019

IEEE Trans. Multimedia

View full text Add to dashboard Cite

In this paper, we propose a multi-level cooperative fusion approach to address the online multiple human tracking problem in a Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter framework. The proposed fusion approach consists essentially of three steps. Firstly, we integrate two human detectors with different characteristics (full-body and bodyparts), and investigate their complementary benefits for tracking multiple targets. For each detector domain, we then propose a novel Discriminative Correlation Matching (DCM) model, and fuse it with spatio-temporal information to address ambiguous identity association in the GM-PHD filter. Finally, we develop a robust fusion center with virtual and real zones to make a global decision based on preliminary candidate targets generated by each detector. This center also mitigates the sensitivity of missed detections in the Generalized Covariance Intersection (GCI) fusion process, thereby improving the fusion performance and tracking consistency. Experiments on the MOTChallenge Benchmark demonstrate the proposed method achieves improved performance over other state-of-the-art RFS based tracking methods.

show abstract

“…Multiple-Object Tracking (MOT) is of crucial importance for applications such as retail video analytics, video surveillance, human-computer interaction, and vehicle navigation. The most common approach to MOT is the tracking-bydetection paradigm [1], [2], [3], [4]; which is comprised of two steps: (1) obtaining potential locations of objects of interest using an object detector and (2) associating these detections to object trajectories.…”

Section: Introductionmentioning

confidence: 99%

A fast multi-object tracking system using an object detector ensemble

Cobos

Hernandez

Abad

2019

2019 IEEE Colombian Conference on Applications in Computational Intelligence (ColCACI)

View full text Add to dashboard Cite

Multiple-Object Tracking (MOT) is of crucial importance for applications such as retail video analytics and video surveillance. Object detectors are often the computational bottleneck of modern MOT systems, limiting their use for real-time applications. In this paper, we address this issue by leveraging on an ensemble of detectors, each running every f frames. We measured the performance of our system in the MOT16 benchmark. The proposed model surpassed other online entries of the MOT16 challenge in speed, while maintaining an acceptable accuracy.

show abstract

Development of a N-type GM-PHD filter for multiple target, multiple type visual tracking

Cited by 40 publications

References 42 publications

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

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

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

A fast multi-object tracking system using an object detector ensemble

Contact Info

Product

Resources

About