Visual tracking with histograms and articulating blocks

Nejhum, S.M. Shahed; Ho, Jeffrey; Yang, Ming–Hsuan

doi:10.1109/cvpr.2008.4587575

Cited by 57 publications

(20 citation statements)

References 20 publications

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“…For example, Adam et al [37] divided the object region into several fragments and located the target's position by fusing the voting maps of these fragments. Nejhum et al [38] modeled the foreground object shape in terms of a small number of rectangular blocks. The algorithm can track the objects by matching foreground intensity histograms and updating the part-based appearance model on-the-fly.…”

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 1 more Smart Citation

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.

show abstract

Section: Fragment-based Trackingmentioning

confidence: 99%

Section: Fragment-based Trackingmentioning

confidence: 99%

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

“…This problem has been thoroughly studied and numerous algorithms have been proposed in the last few decades. Interested readers can refer to [1,2,3,5,10] for a few exemplar implementations. Since this is beyond the scope of this paper, we assume that this step has been done and simply use the annotated bounding box as the one produced by any algorithm that does human detection and tracking.…”

Section: Low-level Feature Extractionmentioning

confidence: 99%

Action Attribute Detection from Sports Videos with Contextual Constraints

Teo

Yang

et al. 2013

Procedings of the British Machine Vision Conference 2013

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In this paper, we are interested in detecting action attributes from sports videos for event understanding and video analysis. Action attribute is a middle layer between low level motion features and high level action classes, which includes various motion patterns of human limbs and bodies and the interaction between human and objects. Successfully detecting action attributes provides a richer video description that facilitates many other important tasks, such action classification, video understanding, automatic video transcript, etc.A naive approach to deal with this challenging problem is to train a classifier for each attribute and then use them to detect attributes in novel videos independently. However, this independence assumption is often too strong, and as we show in our experiments, produces a large number of false positives in practice. We propose a novel approach that incorporates the contextual constraints for activity attribute detection. The temporal contexts within an attribute and the co-occurrence contexts between different attributes are modelled by a factorial conditional random field, which encourages agreement between different time points and attributes. The effectiveness of our methods are clearly illustrated by the experimental evaluations.

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“…The complexity of all these methods increases their computational cost significantly. In addition to the object segmentation, Nejhum et al [15] have used a block configuration for describing the object. Each block corresponds to an intensity histogram and all together share a common configuration.…”

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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Visual tracking with histograms and articulating blocks

Cited by 57 publications

References 20 publications

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

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

Action Attribute Detection from Sports Videos with Contextual Constraints

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

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