Long-range Hand Gesture Recognition with Joint SSD Network

Yi, Chengming; Zhou, Liguang; Wang, Zhixiang; Sun, Zhenglong; Tan, Changgeng

doi:10.1109/robio.2018.8665302

Cited by 9 publications

(3 citation statements)

References 6 publications

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“…Recently, object detection is a popular research area in computer vision, thanks to the emerging of the large-scale labeled data and advanced GPUs. Many excellent algorithms have been developed for object detection like the one-stage object detector, YoloV3 [26], SSD [27] and two-stage object detector, like Joint SSD [28], Faster R-CNN [29], Mask RCNN [30], Cascaded RCNN [31]. The one-stage detector has a higher speed while maintain the similar performance compared with two-stage detector, therefore, the YoloV3 is adopted as the part of object model in DEDUCE [10].…”

Section: B Object Knowledge For Indoor Scene Representationmentioning

confidence: 99%

BORM: Bayesian Object Relation Model for Indoor Scene Recognition

Zhou¹,

Cen²,

Wang³

et al. 2021

Preprint

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Scene recognition is a fundamental task in robotic perception. For human beings, scene recognition is reasonable because they have abundant object knowledge of the real world. The idea of transferring prior object knowledge from humans to scene recognition is significant but still less exploited. In this paper, we propose to utilize meaningful object representations for indoor scene representation. First, we utilize an improved object model (IOM) as a baseline that enriches the object knowledge by introducing a scene parsing algorithm pretrained on the ADE20K dataset with rich object categories related to the indoor scene. To analyze the object co-occurrences and pairwise object relations, we formulate the IOM from a Bayesian perspective as the Bayesian object relation model (BORM). Meanwhile, we incorporate the proposed BORM with the PlacesCNN model as the combined Bayesian object relation model (CBORM) for scene recognition and significantly outperforms the state-of-the-art methods on the reduced Places365 dataset, and SUN RGB-D dataset without retraining, showing the excellent generalization ability of the proposed method. Code can be found at https://github.com/FreeformRobotics/BORM.

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Section: B Object Knowledge For Indoor Scene Representationmentioning

confidence: 99%

BORM: Bayesian Object Relation Model for Indoor Scene Recognition

Zhou¹,

Cen²,

Wang³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…is paper discusses hand gesture recognition in complex environments based on the Single-Shot Multibox Detector. e approach is different from the work [28]. e image pyramid method is adapted to gesture recognition.…”

Section: Introductionmentioning

confidence: 99%

“…e Single-Shot Multibox Detector (SSD) further optimizes object detection [23,24]. As compared to Faster R-CNN, SSD is more simple and efficient as it completely eliminates proposal generation subsequent pixel and feature resampling stages, and it also encapsulates all computation in a single network which makes SSD easily trainable and straightforward to integrate into systems [5,[25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Hand Gesture Recognition Based on Single-Shot Multibox Detector Deep Learning

Liu

Cui

et al. 2019

Mobile Information Systems

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Hand gesture recognition is an intuitive and effective way for humans to interact with a computer due to its high processing speed and recognition accuracy. This paper proposes a novel approach to identify hand gestures in complex scenes by the Single-Shot Multibox Detector (SSD) deep learning algorithm with 19 layers of a neural network. A benchmark database with gestures is used, and general hand gestures in the complex scene are chosen as the processing objects. A real-time hand gesture recognition system based on the SSD algorithm is constructed and tested. The experimental results show that the algorithm quickly identifies humans’ hands and accurately distinguishes different types of gestures. Furthermore, the maximum accuracy is 99.2%, which is significantly important for human-computer interaction application.

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Real-time automated detection of older adults' hand gestures in home and clinical settings

Huang

Tran

Bai

et al. 2022

Neural Comput & Applic

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There is an urgent need, accelerated by the COVID-19 pandemic, for methods that allow clinicians and neuroscientists to remotely evaluate hand movements. This would help detect and monitor degenerative brain disorders that are particularly prevalent in older adults. With the wide accessibility of computer cameras, a vision-based real-time hand gesture detection method would facilitate online assessments in home and clinical settings. However, motion blur is one of the most challenging problems in the fast-moving hands data collection. The objective of this study was to develop a computer vision-based method that accurately detects older adults’ hand gestures using video data collected in real-life settings. We invited adults over 50 years old to complete validated hand movement tests (fast finger tapping and hand opening–closing) at home or in clinic. Data were collected without researcher supervision via a website programme using standard laptop and desktop cameras. We processed and labelled images, split the data into training, validation and testing, respectively, and then analysed how well different network structures detected hand gestures. We recruited 1,900 adults (age range 50–90 years) as part of the TAS Test project and developed UTAS7k—a new dataset of 7071 hand gesture images, split 4:1 into clear: motion-blurred images. Our new network, RGRNet, achieved 0.782 mean average precision (mAP) on clear images, outperforming the state-of-the-art network structure (YOLOV5-P6, mAP 0.776), and mAP 0.771 on blurred images. A new robust real-time automated network that detects static gestures from a single camera, RGRNet, and a new database comprising the largest range of individual hands, UTAS7k, both show strong potential for medical and research applications. Supplementary Information The online version contains supplementary material available at 10.1007/s00521-022-08090-8.

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Long-range Hand Gesture Recognition with Joint SSD Network

Cited by 9 publications

References 6 publications

BORM: Bayesian Object Relation Model for Indoor Scene Recognition

BORM: Bayesian Object Relation Model for Indoor Scene Recognition

Hand Gesture Recognition Based on Single-Shot Multibox Detector Deep Learning

Real-time automated detection of older adults' hand gestures in home and clinical settings

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