A random forest approach to segmenting and classifying gestures

Joshi, Ajjen; Monnier, Camille; Betke, Margrit; Sclaroff, Stan

doi:10.1109/fg.2015.7163126

Cited by 20 publications

(7 citation statements)

References 45 publications

(54 reference statements)

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“…Over the past years, machine learning techniques have become the mainstream to apply for human being detection, such as Adaboost cascade classifiers [30], random forest [31] and support vector machine (SVM) [8]. The ML uses different features, such as colour, texture, contour and pattern, to detect human being.…”

Section: Related Workmentioning

confidence: 99%

Social Video Advertisement Replacement and its Evaluation in Convolutional Neural Networks

Yang

Yu²,

Kumar

et al. 2021

ELCVIA

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This paper introduces a method to use deep convolutional neural networks (CNNs) to automatically replace advertisement (AD) photo on social (or self-media) videos and provides the suitable evaluation method to compare different CNNs. An AD photo can replace a picture inside a video. However, if a human being occludes the replaced picture in the original video, the newly pasted AD photo will block the human occluded part. The deep learning algorithm is implemented to segment the human being from the video. The segmented human pixels are then pasted back to the occluded area, so that the AD photo replacement becomes natural and perfect appearance in the video. This process requires the predicted occlusion edge to be closed to the ground truth occlusion edge, so that the AD photo can be occluded naturally. Therefore, this research introduces a curve fitting method to measure the predicted occlusion edge’s error. By using this method, three CNN methods are applied and compared for the AD replacement. They are mask of regions convolutional neural network (Mask RCNN), a recurrent network for video object segmentation (ROVS) and DeeplabV3. The experimental results show the comparative segmentation accuracy of the different models and DeeplabV3 shows the best performance.

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Section: Related Workmentioning

confidence: 99%

Social Video Advertisement Replacement and its Evaluation in Convolutional Neural Networks

Yang

Yu²,

Kumar

et al. 2021

ELCVIA

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“…Dynamic hand gesture recognition relies on learning temporal (i.e., trajectory, speed) and spatial (i.e., hand shape and location) features for a gesture. There are many techniques that have been used for classification, such as template-based approach [3], [4], statistical methods (Hidden Markov Analysis, Conditional Random Field, and causal analysis) [5]- [8].…”

Section: Related Workmentioning

confidence: 99%

Hand Gesture Recognition in Video Sequences Using Deep Convolutional and Recurrent Neural Networks

Obaid

Babadi

Yoosofan

2020

Applied Computer Systems

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Deep learning is a new branch of machine learning, which is widely used by researchers in a lot of artificial intelligence applications, including signal processing and computer vision. The present research investigates the use of deep learning to solve the hand gesture recognition (HGR) problem and proposes two models using deep learning architecture. The first model comprises a convolutional neural network (CNN) and a recurrent neural network with a long short-term memory (RNN-LSTM). The accuracy of model achieves up to 82 % when fed by colour channel, and 89 % when fed by depth channel. The second model comprises two parallel convolutional neural networks, which are merged by a merge layer, and a recurrent neural network with a long short-term memory fed by RGB-D. The accuracy of the latest model achieves up to 93 %.

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“…The detected 3D fingertip locations are finally used for hand pose estimation with an inverse kinematics solver. The work of (Joshi et al, 2015) use random forest for both segmenting and classifying gesture categories from data coming from different sensors.…”

Section: Approaches Using Non-video Modalities and Multimodal Approachesmentioning

confidence: 99%

Challenges in Multi-modal Gesture Recognition

Escalera

Athitsos

Guyon

2017

The Springer Series on Challenges in Machine Learning

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This paper surveys the state of the art on multimodal gesture recognition and introduces the JMLR special topic on gesture recognition 2011-2015. We began right at the start of the Kinect T M revolution when inexpensive infrared cameras providing image depth recordings became available. We published papers using this technology and other more conventional methods, including regular video cameras, to record data, thus providing a good overview of uses of machine learning and computer vision using multimodal data in this area of application. Notably, we organized a series of challenges and made available several datasets we recorded for that purpose, including tens of thousands of videos, which are available to conduct further research. We also overview recent state of the art works on gesture recognition based on a proposed taxonomy for gesture recognition, discussing challenges and future lines of research.

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A random forest approach to segmenting and classifying gestures

Cited by 20 publications

References 45 publications

Social Video Advertisement Replacement and its Evaluation in Convolutional Neural Networks

Social Video Advertisement Replacement and its Evaluation in Convolutional Neural Networks

Hand Gesture Recognition in Video Sequences Using Deep Convolutional and Recurrent Neural Networks

Challenges in Multi-modal Gesture Recognition

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