No abstract

Recently, automatic hand gesture recognition has gained increasing importance for two principal reasons: the growth of the deaf and hearing-impaired population, and the development of visionbased applications and touchless control on ubiquitous devices. As hand gesture recognition is at the core of sign language analysis a robust hand gesture recognition system should consider both spatial and temporal features. Unfortunately, finding discriminative spatiotemporal descriptors for a hand gesture sequence is not a trivial task. In this study, we proposed an efficient deep convolutional neural networks approach for hand gesture recognition. The proposed approach employed transfer learning to beat the scarcity of a large labeled hand gesture dataset. We evaluated it using three gesture datasets from color videos: 40, 23, and 10 classes were used from these datasets. The approach obtained recognition rates of 98.12%, 100%, and 76.67% on the three datasets, respectively for the signer-dependent mode. For the signer-independent mode, it obtained recognition rates of 84.38%, 34.9%, and 70% on the three datasets, respectively. INDEX TERMS 3DCNN, computer vision, deep learning, hand gesture recognition, sign language recognition, transfer learning.

Hand gesture recognition is an attractive research field with a wide range of applications, including video games and telesurgery techniques. Another important application of hand gesture recognition is the translation of sign language, which is a complicated structured form of hand gestures. In sign language, the fingers' configuration, the hand's orientation, and the hand's relative position to the body are the primitives of structured expressions. The importance of hand gesture recognition has increased due to the prevalence of touchless applications and the rapid growth of the hearing-impaired population. However, developing an efficient recognition system needs to overcome the challenges of hand segmentation, local hand shape representation, global body configuration representation, and gesture sequence modeling. In this paper, a novel system is proposed for dynamic hand gesture recognition using multiple deep learning architectures for hand segmentation, local and global feature representations, and sequence feature globalization and recognition. The proposed system is evaluated on a very challenging dataset, which consists of 40 dynamic hand gestures performed by 40 subjects in an uncontrolled environment. The results show that the proposed system outperforms stateof-the-art approaches, demonstrating its effectiveness.

Date is the main fruit crop of the Kingdom of Saudi Arabia (KSA), approximately covering 72% of the total area under permanent crops. The Food and Agriculture Organization states that date production worldwide was 3

The date palm is one of the most valuable fruit trees in the world. Most methods used for date fruit inspection, harvesting, grading, and classification are manual, which makes them ineffective in terms of both time and economy. Research on automated date fruit harvesting is limited as there is no public dataset for date fruits to aid in this. In this work, we present a comprehensive dataset for date fruits that can be used by the research community for multiple tasks including automated harvesting, visual yield estimation, and classification tasks. The dataset contains images of date fruit bunches of different date varieties, captured at different pre-maturity and maturity stages. These images cover multiple sets of variations such as multi-scale images, variable illumination, and different bagging states. We also marked date bunches for selected palms and measured the weights of the bunches, captured their images on a graph paper, and recorded 360° video of the palms. This dataset can help in advancing research and automating date palm agricultural applications, including robotic harvesting, fruit detection and classification, maturity analysis, and weight/yield estimation. The dataset is freely and publicly available for the research community in the IEEE DataPort repository [1] (https://doi.org/10.21227/x46j-sk98).