Playing into the wild: A gesture-based interface for gaming in public spaces

Roccetti, Marco; Marfia, Gustavo; Semeraro, Angelo

doi:10.1016/j.jvcir.2011.12.006

Cited by 78 publications

(32 citation statements)

References 18 publications

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“…For each sequence, freely available on the Internet, 1 we provide a hand-labeled ground truth. For the best of authors' knowledge, we propose the first RGB-D benchmark dataset with handlabeled ground truth that includes sequences with different challenging scenarios for FgjBg segmentation.…”

Section: Benchmark Data and Resultsmentioning

confidence: 99%

“…Devices such as the Microsoft's Kinect or the Asus's Xtion Pro, that cost less than 200 dollars, can provide real time (RÍ30 frames per second) depth and color information with good resolution (VGA format); this attractive trade-off between cost and performance is not achievable by any other kind of depth cameras in the market, such as stereo or time of flight cameras. Due to these features, RGB-D cameras have been rapidly employed in several command-less applications such as gaming [1] and human computer interface platforms [2]. Moreover, they have been employed in other computer vision research areas such as human body tracking for elderly activity monitoring [3] and 3D object recognition [4].…”

Section: Introductionmentioning

confidence: 99%

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Background foreground segmentation with RGB-D Kinect data: An efficient combination of classifiers

Camplani

Salgado

2014

Journal of Visual Communication and Image Representation

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Low cost RGB-D cameras such as the Microsoft's Kinect or the Asus's Xtion Pro are completely changing the computer vision world, as they are being successfully used in several applications and research areas. Depth data are particularly attractive and suitable for applications based on moving objects detection through foreground/background segmentation approaches; the RGB-D applications proposed in literature employ, in general, state of the art foreground/background segmentation techniques based on the depth information without taking into account the color information. The novel approach that we propose is based on a combination of classifiers that allows improving background subtraction accuracy with respect to state of the art algorithms by jointly considering color and depth data. In particular, the combination of classifiers is based on a weighted average that allows to adaptively modifying the support of each classifier in the ensemble by considering foreground detections in the previous frames and the depth and color edges. In this way, it is possible to reduce false detections due to critical issues that can not be tackled by the individual classifiers such as: shadows and illumination changes, color and depth camouflage, moved background objects and noisy depth measurements. Moreover, we propose, for the best of the author's knowledge, the first publicly available RGB-D benchmark dataset with hand-labeled ground truth of several challenging scenarios to test background/foreground segmentation algorithms.

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Section: Benchmark Data and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Background foreground segmentation with RGB-D Kinect data: An efficient combination of classifiers

Camplani

Salgado

2014

Journal of Visual Communication and Image Representation

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“…In this paper, the proposed algorithm segmented image sector to estimate the weight of a ginseng and experiments have shown that this method is effective for weight estimation. This image segmentation method is used to recognize the postures and gestures of the hand and body for using computer based vision system [13,14]. Following the weight-estimation procedure, the group of candidates determined as being in Grade 1 was again evaluated using shape analysis and each candidate assigned a grade between one and three.…”

Section: Discussionmentioning

confidence: 99%

Development of an automatic sorting system for fresh ginsengs by image processing techniques

Jeong

Lee

2017

Hum. Cent. Comput. Inf. Sci.

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This study was conducted with the objective of implementing a smart IoT (internet of things) factory consisting of an automatic 6-year-old fresh ginseng grade classification device. Conventionally, washed 6-year-old ginseng from farmlands is manually sorted into three grades using classification criteria such as weight and shape. However, the cost associated with this classification process has been on the increase. Consequently, to reduce this associated cost, we developed an automatic ginseng sorting device that classifies 6-year-old ginseng according to weight and shape via image processing and sends the classification results to a factory server over a network. Evaluations conducted of the performance of the developed machine using 100 units of 6-year-old ginseng showed that it has a high recognition rate, with an accuracy of 94% for Grade 1, 98% for Grade 2, and 90% for Grade 3.

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“…While the method can't acquire the optimal result for all the situations, the approach can acquire relatively good result especially some specified challenges that alternative methods can't handle. And owing to the heavy computational complexity, our algorithm cannot be applied to the pratical application so far, but we can take advantage of the alrorithms which have implemented in practical applications just like the literatures [12,14] say the Hough Transform and the particle filter, and the approaches of foreground and background segmentation in [5,6]. These approaches are very useful that help our algorithm to be implemented in the practical world.…”

Section: Introductionmentioning

confidence: 99%

Robust object tracking via local constrained and online weighted

Zha

Cao

Huang

et al. 2015

Multimed Tools Appl

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Accounting for most recent tracking algorithms just only handle one specified challenge, in order to adjust to diverse scenarios in object tracking, we propose a discriminative tracking algorithm based on a collaborative model. In order to account for drastic appearance change, the visual prior have been learned offline by adding the locality regularization term. We transfer the visual prior to represent object and learn a basic discriminative classifier. Next we employ minimal sparse reconstruction error to find the best candidate with the learned classifier. In addition, we derive a parameter update strategy which is based on the candidates' distribution. With this strategy, the candidates' weight can be calculated according to the candidates' distribution online. The tracking is carried out within a Bayesian inference framework with this representation. We use the learned classifier and sparse template to construct the dynamic parameter observation model. Furthermore, the particle filter is used to estimate the tracking result sequentially. Both qualitative and quantitative evaluations on variety of challenging benchmark sequences demonstrate that the proposed tracking algorithm achieves more robust object tracking than the state-of-the-art methods.

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Playing into the wild: A gesture-based interface for gaming in public spaces

Cited by 78 publications

References 18 publications

Background foreground segmentation with RGB-D Kinect data: An efficient combination of classifiers

Background foreground segmentation with RGB-D Kinect data: An efficient combination of classifiers

Development of an automatic sorting system for fresh ginsengs by image processing techniques

Robust object tracking via local constrained and online weighted

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