Online multiple object tracking with the hierarchically adopted GM-PHD filter using motion and appearance

Song, Young-min; Jeon, Moongu

doi:10.1109/icce-asia.2016.7804800

Cited by 46 publications

(38 citation statements)

References 9 publications

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“…The ablation study proves that GMPHD-OGM (w/ SIOA) is more efficient to solve the defined problems than the given baseline methods such as GMPHD-HDA and GMPHD-OGM (w/ IOU). GMPHD-OGM achieves the best MOTA scores in MOT15 and MOT17 datasets, respectively, in comparison with the PHD filter based online trackers [16], [18], [22], [38], [41]- [43]. Finally, by the comprehensive evaluation, we conclude the proposed tracker (GMPHD-OGM) against state-of-the-art algorithms including online with DNN even including offline, GMPHD-OGM shows the competitive value in "MOTA versus speed".…”

Section: Discussionmentioning

confidence: 72%

“…We conclude this paper with future work in Section VI. Some preliminary results of this work was presented in Song et al [16], [17].…”

mentioning

confidence: 81%

“…As demand increases on online and real-time tracker in video-based tracking system, the PHD filter have been an emerging tracking model, recently. Song et al [16] extended the GMPHD filter based tracking with the two-stage hierarchical data association strategy to recover fragmented and lost tracks. They defined the affinity in the track-to-track association step by using tracks' linear motion and color histogram appearance.…”

Section: Related Workmentioning

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%

“…V. Eiselein et al [43] combined the feature-based label tree to the Gaussian mixture PHD (GMPHD) filter, which use visual features to help the GMPHD filter work sensibly in video data system. Song et al [16] extended the GMPHD filter based tracking with the two-stage hierarchical data association strategy and use simple motion estimation and appearance matching to recover lost tracks. T. Kutschbach et al [42] joined the GMPHD filter with the kernelized correlation filters (KCF) [53] for online appearance update to overcome occlusion.…”

mentioning

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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