Bayesian Multi-object Tracking Using Motion Context from Multiple Objects

Yoon, Ju Hong; Yang, Ming–Hsuan; Lim, Jongwoo; Yoon, Kuk-Jin

doi:10.1109/wacv.2015.12

Cited by 175 publications

(101 citation statements)

References 25 publications

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“…the motion of interacting pedestrians is considered in this way, the binary group membership represented by the model neglects potential correlations between subjects from different groups. Pellegrini et al (2009), Choi andSavarese (2010), and Yoon et al (2015) do not apply grouping explicitly. They predict the position of each subject based on the history of all pedestrians.…”

Section: Related Workmentioning

confidence: 99%

“…As a consequence, potential correlations between subjects that are further apart are neglected. Yoon et al (2015) also consider the relative motion between subjects by conditioning the current state estimate on the previous state estimate of the same subject and on the subjects in its vicinity. In this way, the motion of different interrelated persons is taken into account, but uncertainties about the previous state estimates are not considered in the estimation of the current state estimates.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Gaussian Process Based Multi-Person Interaction Model

Klinger¹,

Rottensteiner²,

Heipke³

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Online multi-person tracking in image sequences is commonly guided by recursive filters, whose predictive models define the expected positions of future states. When a predictive model deviates too much from the true motion of a pedestrian, which is often the case in crowded scenes due to unpredicted accelerations, the data association is prone to fail. In this paper we propose a novel predictive model on the basis of Gaussian Process Regression. The model takes into account the motion of every tracked pedestrian in the scene and the prediction is executed with respect to the velocities of all interrelated persons. As shown by the experiments, the model is capable of yielding more plausible predictions even in the presence of mutual occlusions or missing measurements. The approach is evaluated on a publicly available benchmark and outperforms other state-of-the-art trackers.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Gaussian Process Based Multi-Person Interaction Model

Klinger¹,

Rottensteiner²,

Heipke³

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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show abstract

“…This hints at a lack of specialization of the appearance models, which could be addressed by designing features that can better represent instances. Another solution to this issue would consist in complementing our method with advanced inter-target and occlusion reasoning, e.g., [48].…”

Section: Pascal-to-kitti: Domain Adaptation In Motmentioning

confidence: 99%

Online Domain Adaptation for Multi-Object Tracking

Gaidon¹,

Vig²

2015

Procedings of the British Machine Vision Conference 2015

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Automatically detecting, labeling, and tracking objects in videos depends first and foremost on accurate category-level object detectors. These might, however, not always be available in practice, as acquiring high-quality large scale labeled training datasets is either too costly or impractical for all possible real-world application scenarios. A scalable solution consists in re-using object detectors pre-trained on generic datasets. This work is the first to investigate the problem of on-line domain adaptation of object detectors for causal multi-object tracking (MOT). We propose to alleviate the dataset bias by adapting detectors from category to instances, and back: (i) we jointly learn all target models by adapting them from the pre-trained one, and (ii) we also adapt the pre-trained model on-line. We introduce an on-line multi-task learning algorithm to efficiently share parameters and reduce drift, while gradually improving recall. Our approach is applicable to any linear object detector, and we evaluate both cheap "mini-Fisher Vectors" and expensive "off-the-shelf" ConvNet features. We quantitatively measure the benefit of our domain adaptation strategy on the KITTI tracking benchmark and on a new dataset (PASCAL-to-KITTI) we introduce to study the domain mismatch problem in MOT.

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“…In the literature, the problem is addressed by a large variety of approaches, from online (or single-scan) techniques [1][2][3][4] where only the previous frames are considered, to offline approaches using past and future frames. Among offline techniques, global approaches perform the data association over all the frames simultaneously or by batch [5][6][7][8][9][10][11][12][13][14][15], whereas sliding window (a.k.a.…”

Section: Introductionmentioning

confidence: 99%

Improving Multi-frame Data Association with Sparse Representations for Robust Near-online Multi-object Tracking

Fagot-Bouquet

Audigier

Dhome

et al. 2016

Lecture Notes in Computer Science

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Abstract. Multiple Object Tracking still remains a difficult problem due to appearance variations and occlusions of the targets or detection failures. Using sophisticated appearance models or performing data association over multiple frames are two common approaches that lead to gain in performances. Inspired by the success of sparse representations in Single Object Tracking, we propose to formulate the multi-frame data association step as an energy minimization problem, designing an energy that efficiently exploits sparse representations of all detections. Furthermore, we propose to use a structured sparsity-inducing norm to compute representations more suited to the tracking context. We perform extensive experiments to demonstrate the effectiveness of the proposed formulation, and evaluate our approach on two public authoritative benchmarks in order to compare it with several state-of-the-art methods.

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Bayesian Multi-object Tracking Using Motion Context from Multiple Objects

Cited by 175 publications

References 25 publications

A Gaussian Process Based Multi-Person Interaction Model

A Gaussian Process Based Multi-Person Interaction Model

Online Domain Adaptation for Multi-Object Tracking

Improving Multi-frame Data Association with Sparse Representations for Robust Near-online Multi-object Tracking

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