Tracking Using Superpixel Features

Liu, Jingjing; Chen, Ying; Zha, Cheng; Yu, Hua; Zhao, Li

doi:10.1109/icmtma.2016.211

Cited by 4 publications

(5 citation statements)

References 12 publications

(10 reference statements)

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“…(3) Postprocessing block size by 2 1 gives the next block size as 5 × 5, followed by 9 × 9 by increasing the block size by 2 2 , and so on. More generally, the size of the square blocks τ i (along a single spatial dimension) for the i-th iteration can be defined by a single number:…”

Section: A Implicit Identification Of Boundariesmentioning

confidence: 99%

“…This segmentation is quite useful in summarizing the content of an image into regions with boundaries that adhere to the contours in the image. They are widely used for a large number of applications such as object tracking [1], image segmentation [2], video stream analysis [3], or even computer vision based physio-logical measurements [4].…”

Section: Introductionmentioning

confidence: 99%

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Iterative Boundaries Implicit Identification for Superpixels Segmentation: A Real-Time Approach

Bobbia¹,

Macwan²,

Benezeth³

et al. 2021

IEEE Access

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Superpixel algorithms group visually coherent pixels and form an alternative representation of the regular structure of the pixel grid. This fundamental low-level computer vision preprocessing step greatly reduces the complexity of subsequent image processing tasks. However, most of the existing methods suffer from very high calculation costs which makes them quite unsuitable for time-sensitive applications. In this paper, we propose a new superpixel segmentation method, named IBIS for Iterative Boundaries implicit Identification for superpixels segmentation, that implicitly identifies the boundaries between superpixels and performs the segmentation using only a fraction of the pixels of the input image, thereby reducing the complexity and computation time. The results obtained during the experiments show that the segmentation quality of IBIS is comparable to that of state of the art methods with a computation time divided by a factor of 8 without parallelization of the processing for low resolution images (e.g., 320x240 pixels) as usually provided in public data sets. We also present and comprehensively evaluate the GPU variant of IBIS named IBIScuda that allows an optimal exploitation of the available resources considering the limited bandwidth between CPU and GPU memories.

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Section: A Implicit Identification Of Boundariesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Iterative Boundaries Implicit Identification for Superpixels Segmentation: A Real-Time Approach

Bobbia¹,

Macwan²,

Benezeth³

et al. 2021

IEEE Access

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“…Superpixels algorithms perform segmentation in order to group pixels in a coherent way with respect to the edges. They are used for a wide range of applications such as object tracking [14], image segmentation [19], video representation [5] or for biomedical applications [3]. The clusters are established based on various scenarios.…”

Section: Efficient Superpixels Segmentationmentioning

confidence: 99%

Real-Time Temporal Superpixels for Unsupervised Remote Photoplethysmography

Bobbia

Luguern

Benezeth

et al. 2018

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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Segmentation is a critical step for many computer vision applications. Among them, the remote photoplethysmography technique is significantly impacted by the quality of region of interest segmentation. With the heart-rate estimation accuracy, the processing time is obviously a key issue for real-time monitoring. Recent face detection algorithms can perform real-time processing, however for unsupervised algorithms, i.e. without any subject detection based on supervised learning, existing methods are not able to achieve real-time on regular platform. In this paper, we propose a new method to perform real-time unsupervised remote photoplethysmograhy based on efficient temporally propagated superpixels segmentation. The proposed method performs the segmentation step by implicitly identifying the superpixel boundaries. Hence, only a fraction of the image is used to perform the segmentation which reduces greatly the computational burden of the process. The segmentation quality remains comparable to state of the art methods while computational time is divided by a factor up to 8 times. The efficiency of the superpixel segmentation allow us to propose a real-time unsupervised rPPG algorithm considering frames of 640x480, RGB, at 25 frames per second on a single core platform. We obtained realtime processing for 93% of precision at 2.5 beat per minute using our inhouse video database.

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“…A superpixel based representation got much attention by computer vision community for object recognition [59], human detection [118], activity recognition [162], and image segmentation [2]. Numerous tracking algorithms have been developed using superpixels [81], [82], [96], [155], [156]. The tracker introduced by Jingjing et al [82] is based on Bayesian framework.…”

Section: E Superpixel Based Trackermentioning

confidence: 99%

“…Numerous tracking algorithms have been developed using superpixels [81], [82], [96], [155], [156]. The tracker introduced by Jingjing et al [82] is based on Bayesian framework. The model is trained over target and background superpixels.…”

Section: E Superpixel Based Trackermentioning

confidence: 99%

Tracking Noisy Targets: A Review of Recent Object Tracking Approaches

Fiaz¹,

Mahmood²,

Jung³

2018

Preprint

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Visual object tracking is an important computer vision problem with numerous real-world applications including human-computer interaction, autonomous vehicles, robotics, motion-based recognition, video indexing, surveillance and security. In this paper, we aim to extensively review the latest trends and advances in the tracking algorithms and evaluate the robustness of trackers in the presence of noise. The first part of this work comprises a comprehensive survey of recently proposed tracking algorithms. We broadly categorize trackers into correlation filter based trackers and the others as noncorrelation filter trackers. Each category is further classified into various types of trackers based on the architecture of the tracking mechanism. In the second part of this work, we experimentally evaluate tracking algorithms for robustness in the presence of additive white Gaussian noise. Multiple levels of additive noise are added to the Object Tracking Benchmark (OTB) 2015, and the precision and success rates of the tracking algorithms are evaluated. Some algorithms suffered more performance degradation than others, which brings to light a previously unexplored aspect of the tracking algorithms. The relative rank of the algorithms based on their performance on benchmark datasets may change in the presence of noise. Our study concludes that no single tracker is able to achieve the same efficiency in the presence of noise as under noise-free conditions; thus, there is a need to include a parameter for robustness to noise when evaluating newly proposed tracking algorithms.

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Tracking Using Superpixel Features

Cited by 4 publications

References 12 publications

Iterative Boundaries Implicit Identification for Superpixels Segmentation: A Real-Time Approach

Iterative Boundaries Implicit Identification for Superpixels Segmentation: A Real-Time Approach

Real-Time Temporal Superpixels for Unsupervised Remote Photoplethysmography

Tracking Noisy Targets: A Review of Recent Object Tracking Approaches

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