Tracking of a non-rigid object via patch-based dynamic appearance modeling and adaptive Basin Hopping Monte Carlo sampling

Kwon, Junseok; Lee, Kyoung Mu

doi:10.1109/cvpr.2009.5206502

Cited by 167 publications

(155 citation statements)

References 13 publications

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“…The tracker updates the classifier and the classifier reinitializes it in case of a drift. Similarly in [21], the appearance model of the tracker evolves during time. All the above approaches present mechanisms for preventing the drifting effect in some form.…”

Section: Related Workmentioning

confidence: 99%

Segmentation Based Particle Filtering for Real-Time 2D Object Tracking

Belagiannis

Schubert²,

Navab

et al. 2012

Computer Vision – ECCV 2012

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Abstract. We address the problem of visual tracking of arbitrary objects that undergo significant scale and appearance changes. The classical tracking methods rely on the bounding box surrounding the target object. Regardless of the tracking approach, the use of bounding box quite often introduces background information. This information propagates in time and its accumulation quite often results in drift and tracking failure. This is particularly the case with the particle filtering approach that is often used for visual tracking. However, it always uses a bounding box around the object to compute features of the particle samples. Since this causes the drift, we propose to use segmentation for sampling. Relying on segmentation and computing the colour and gradient orientation histograms from these segmented particle samples allows the tracker to easily adapt to the object's deformations, occlusions, orientation, scale and appearance changes. We propose two particle sampling strategies based on segmentation. In the first, segmentation is done for every propagated particle sample, while in the second only the strongest particle sample is segmented. Depending on this decision there is obviously a trade-off between speed and performance. We perform an exhaustive quantitative evaluation on a number of challenging sequences and compare our method with the number of stateof-the-art methods previously evaluated on those sequences. The results we obtain outperform majority of the related work, both in terms of the performance and speed.

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Section: Related Workmentioning

confidence: 99%

Segmentation Based Particle Filtering for Real-Time 2D Object Tracking

Belagiannis

Schubert²,

Navab

et al. 2012

Computer Vision – ECCV 2012

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“…The algorithm can track the objects by matching foreground intensity histograms and updating the part-based appearance model on-the-fly. Kwon et al [39] represented an object by a fixed number of local patches and updated the model during tracking. In contrast, Felzenszwalb et al [48] developed a multi-scale deformable part model to detect and localize objects of a generic category.…”

Section: Fragment-based Trackingmentioning

confidence: 99%

“…For most of the earlier reported fragment-based algorithms [37][38][39][40][41][42][43][44][45][46][47], each fragment is independently tracked based on features matching, and the whole object is tracked using linear weighting scheme, vote map, or maximum similarity of the fragment location. For most of the recently reported algorithms [48][49][50][51][52][53][54][55][56][57][58][59]58], in contrast, spatial constraints between these fragments are often imposed, and these algorithms are more robust to deformation and illumination changes.…”

Section: Fragment-based Trackingmentioning

confidence: 99%

Structured fragment-based object tracking using discrimination, uniqueness, and validity selection

Zheng

Xin

et al. 2017

Multimedia Systems

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Local features have widely been used in visual tracking to improve robustness in the presence of partial occlusion, deformation, and rotation. In this paper, a local fragment-based object tracking algorithm is proposed. Unlike many existing fragment-based algorithms using all the fragments and allocating the weight to each fragment according to similarity, the proposed algorithm only selects discriminative, unique, and valid fragments for tracking. First, discrimination and uniqueness metric are defined for each local fragment, and an automatic pre-selection mechanism is proposed for all these fragments. Second, a Harris-SIFT filter is used to select the current valid fragments and exclude the occluded or highly deformed fragments. By selecting the discriminative, unique, and valid fragments, these fragments are used to construct a structured description for the object. Finally, the object tracking is performed using the selected fragments combining the displacement and similarity, as well as spatial constraint of the selected fragments. The object template can be updated by fusing feature similarity and structural consistency. The experimental results on a recent OTB 2013 tracking benchmark data set demonstrate that the proposed algorithm can achieve reliable tracking results even in the presence of significant appearance changes, partial occlusion, and similar disturbances.

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“…For example, Nejhum et al [29] proposed to track articulated objects with a set of independent rectangular blocks that are used in a refinement step to segment the object with a graphcut algorithm. Similarly, although not segmenting the object, Kwon et al [24] handle deforming objects by tracking configurations of a dynamic set of image patches, and they use Basin Hopping Monte Carlo (BHMC) sampling to reduce the computational complexity. Other approaches [33,40] use a segmentation on the superpixel level.…”

Section: Related Workmentioning

confidence: 99%