Robust and efficient fragments-based tracking using mean shift

Wang, Fanglin; Yu, Shengyang; Yang, Jie

doi:10.1016/j.aeue.2009.04.004

Cited by 29 publications

(25 citation statements)

References 11 publications

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“…It should be noted, for improving the efficiency, that many fragment-based tracking algorithms consider that the position of each fragment with respect to the center of the object is rigid or even fixed [37,[41][42][43][44][45][46][47], or their relative positions among the patches are rigid [51]. This assumption inevitably reduces the flexibility against deformation.…”

Section: Fragment-based Trackingmentioning

confidence: 99%

“…However, an individual fragment may have a larger chance of drifting due to the unreliable similarity scores, which is particularly severe in the presence of a cluttered background. Although some algorithms employ a linear-weighted location scheme of multi-fragments [40,[42][43][44][45][46][47], their performance is degraded as the number of unreliable fragments increases in complex environments. In these situations, the similarity scores of incorrectly tracked fragments become unreliable, where the use of many such fragments would affect tracking performance in a negative way.…”

Section: Fragment-based Trackingmentioning

confidence: 99%

“…The partitions of fragment are either overlapping [37, 40-42, 45, 47] or non-overlapping [29,43,44,46,[49][50][51][52][53][54][55][56]. In general, color and gradient are the most useful features, where the effects of overlapping or non-overlapping partition do not play a major role.…”

Section: Fragment-based Trackingmentioning

confidence: 99%

“…Moreover, for most of the algorithms, each fragment is equally treated and a different weight is assigned only based on its contribution [37, 40-47, 51, 53, 56]. To estimate the contribution of each fragment, the background is used, but the differences among the object fragments are ignored [41,43,45]. As a matter of fact, similar object fragments may cause confusion and ultimately lead to incorrect tracking result.…”

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%

See 4 more Smart Citations

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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Section: Fragment-based Trackingmentioning

confidence: 99%

Section: Fragment-based Trackingmentioning

confidence: 99%

Section: Fragment-based Trackingmentioning

confidence: 99%

Section: Fragment-based Trackingmentioning

confidence: 99%

Section: Fragment-based Trackingmentioning

confidence: 99%

See 3 more Smart Citations

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

Zheng

Xin

et al. 2017

Multimedia Systems

View full text Add to dashboard Cite

show abstract

Robust Object Tracking Using Valid Fragments Selection

Zheng

Tian

et al. 2016

MultiMedia Modeling

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Local features are widely used in visual tracking to improve robustness in cases of partial occlusion, deformation and rotation. This paper proposes a local fragment-based object tracking algorithm. Unlike many existing fragment-based algorithms that allocate the weights to each fragment, this method firstly defines discrimination and uniqueness for local fragment, and builds an automatic pre-selection of useful fragments for tracking. Then, a Harris-SIFT filter is used to choose the current valid fragments, excluding occluded or highly deformed fragments. Based on those valid fragments, fragment-based color histogram provides a structured and effective description for the object. Finally, the object is tracked using a valid fragment template combining the displacement constraint and similarity of each valid fragment. The object template is updated by fusing feature similarity and valid fragments, which is scale-adaptive and robust to partial occlusion. The experimental results show that the proposed algorithm is accurate and robust in challenging scenarios.

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