Data-Driven Probabilistic Occlusion Mask to Promote Visual Tracking

Meshgi, Kourosh; Maeda, Shingo; Oba, Shigeyuki; Ishii, Shin

doi:10.1109/crv.2016.19

Cited by 7 publications

(11 citation statements)

References 30 publications

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“…The weighted patch descriptor [10] is more flexible for partial occlusion and appearance changes of the object. Similar to SOWP, some methods [41,42] weight the sample with patch likelihoods in particle filters, and [43] used adaptive weighting in patch-based correlation filters to mitigate the effect of occlusion. However, when the object is occluded for a long time, SOWP still has drift, and it is difficult to distinguish target and background when there are some appearance similarities brought by background blur or target occlusion in the bounding box.…”

Section: Related Workmentioning

confidence: 99%

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Minimum Barrier Distance-Based Object Descriptor for Visual Tracking

Guo

et al. 2018

Applied Sciences

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In most visual tracking tasks, the target is tracked by a bounding box given in the first frame. The complexity and redundancy of background information in the bounding box inevitably exist and affect tracking performance. To alleviate the influence of background, we propose a robust object descriptor for visual tracking in this paper. First, we decompose the bounding box into non-overlapping patches and extract the color and gradient histograms features for each patch. Second, we adopt the minimum barrier distance (MBD) to calculate patch weights. Specifically, we consider the boundary patches as the background seeds and calculate the MBD from each patch to the seed set as the weight of each patch since the weight calculated by MBD can represent the difference between each patch and the background more effectively. Finally, we impose the weight on the extracted feature to get the descriptor of each patch and then incorporate our MBD-based descriptor into the structured support vector machine algorithm for tracking. Experiments on two benchmark datasets demonstrate the effectiveness of the proposed approach.

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Section: Related Workmentioning

confidence: 99%

“…The red number is the best result. We also compared our tracker with the occlusion probability mask-based tracker (OPM) [41]. We ran our method on the same sequences and adopted the same average center location error for comparing with this method, and the results are shown in Table 6.…”

Section: Evaluation On Otb-100mentioning

confidence: 99%

Minimum Barrier Distance-Based Object Descriptor for Visual Tracking

Guo

et al. 2018

Applied Sciences

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“…To address this, researchers came by different approaches to provide good samples for tracking using context [18,23], saliency maps [25], confidence maps [44], and optical flow [22]. Adaptive weights for the samples based on their appearance similarity to the target [35], occlusion state [30], and spatial distance to previous target location [47] have also been considered, however, selecting an efficient subset for classifier re-training have been ignored, as most of the trackers retrain on all of the data, a randomized subset of it [32], or in special cases re-sample the training data based on their boosting value [28]. A "clean" subset of training samples to re-train the classifier can achieve much higher performance than the full set [36,51], therefore, a principled ordering and selection of the samples reduces the cost of labeling and accelerate the performance with smaller re-training sample size [45].…”

Section: Related Workmentioning

confidence: 99%

Active collaborative ensemble tracking

Meshgi

Mirzaei

Oba

et al. 2017

2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

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A discriminative ensemble tracker employs multiple classifiers, each of which casts a vote on all of the obtained samples. The votes are then aggregated in an attempt to localize the target object. Such method relies on collective competence and the diversity of the ensemble to approach the target/non-target classification task from different views. However, by updating all of the ensemble using a shared set of samples and their final labels, such diversity is lost or reduced to the diversity provided by the underlying features or internal classifiers' dynamics. Additionally, the classifiers do not exchange information with each other while striving to serve the collective goal, i.e., better classification. In this study, we propose an active collaborative information exchange scheme for ensemble tracking. This, not only orchestrates different classifier towards a common goal but also provides an intelligent update mechanism to keep the diversity of classifiers and to mitigate the shortcomings of one with the others. The data exchange is optimized with regard to an ensemble uncertainty utility function, and the ensemble is updated via co-training. The evaluations demonstrate promising results realized by the proposed algorithm for the real-world online tracking.

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“…The most general type of tracking is single-object model-free online tracking, in which the object is annotated in the first frame, and tracked in the subsequent frames with no prior knowledge about the target's appearance, its motions, the background, the configurations of the camera, and other conditions of the scene. Visual tracking is still considered as a challenging problem despite that many efforts have been made to address abrupt appearance changes of the target [1], its sophisticated transformations [2] and deformations [3], background clutter [4], occlusion [5], and motion artifacts [6].…”

Section: Introductionmentioning

confidence: 99%

“…To address these issues, various solutions have been introduced in the literature, yet a comprehensive solution is to be proposed. Adaptive weights for the samples based on their appearance similarity to the target [8], occlusion state [5], [24], and spatial distance to previous target location [25] have been considered, especially in the context of trackingby-detection, boosting [26], [27] have been extensively investigated [18], [28], [29]. On the other hand, mistakes of the labeler manifest themselves as label noise that confuses the classifier.…”

Section: Introductionmentioning

confidence: 99%

Efficient Version-Space Reduction for Visual Tracking

Meshgi

Oba

Ishii

2017

2017 14th Conference on Computer and Robot Vision (CRV)

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Discrminative trackers, employ a classification approach to separate the target from its background. To cope with variations of the target shape and appearance, the classifier is updated online with different samples of the target and the background. Sample selection, labeling and updating the classifier is prone to various sources of errors that drift the tracker. We introduce the use of an efficient version space shrinking strategy to reduce the labeling errors and enhance its sampling strategy by measuring the uncertainty of the tracker about the samples. The proposed tracker, utilize an ensemble of classifiers that represents different hypotheses about the target, diversify them using boosting to provide a larger and more consistent coverage of the version-space and tune the classifiers' weights in voting. The proposed system adjusts the model update rate by promoting the co-training of the short-memory ensemble with a long-memory oracle. The proposed tracker outperformed state-of-the-art trackers on different sequences bearing various tracking challenges.

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Data-Driven Probabilistic Occlusion Mask to Promote Visual Tracking

Cited by 7 publications

References 30 publications

Minimum Barrier Distance-Based Object Descriptor for Visual Tracking

Minimum Barrier Distance-Based Object Descriptor for Visual Tracking

Active collaborative ensemble tracking

Efficient Version-Space Reduction for Visual Tracking

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