King Fahd University of Petroleum and Minerals

King Abdulaziz City for Science and Technology

Saudi Data and Artificial Intelligence Authority (sdaia)

Hamzah Luqman

Sabri A Mahmoud

El-Sayed M El-Alfy

Motaz Alfarraj

Sabri Abdullah Mohammed

Sadam Hussein Al-Azani

Galal M Binmakhashen

Ayaz Ul Hassan Khan

Ala Elddin Sidig

Dina Hamed Al-Hamed

A sign language recognition system is described. The system includes a motion sensor, a processing circuitry and a display device. The motion sensor captures and records a dynamic sign language gesture as a sign video stream. The processing circuitry is configured with a key postures extractor, an accumulative video motion (AVM), and a sign recognition network (SRN). The key postures extractor captures main postures of the dynamic sign language gesture by extracting key frames in the sign video stream. The AVM captures motion of the sign video stream frames and transforms the motion in an AVM frame into a single AVM image. The SRN is configured as a convolutional network. The main postures and AVM image are fed into a two-stream network. The features from the two stream network are concatenated and fed into the SRN for learning fused features and performing classification of the sign language gesture.

Efficient two-stream network system and method for isolated sign language recognition using accumulative video motion

A system and method for real-time monitoring of personal protective equipment (PPE) compliance at a worksite. The system includes a processor, a host memory, a graphical processing unit (GPU), a GPU memory, a storage device, an input device, a plurality of cameras, and a display device. The system creates multiple inference engines and loads input frames from the storage device for pre-processing. The pre-processed frames are processed to obtain annotations and generate an output file. The system also includes a preparatory area and an operations area with cameras configured to capture and transmit video frames. The system can process input frames at a rate of at least 15 frames per second, providing real-time PPE compliance information.

Real-time monitoring system for personal protective equipment compliance at worksites

Methods, systems, and computer readable media for recognizing one or more hand gestures of a hand-based signal or conversation are described. Some implementations include obtaining one or more unprocessed images of the hand-based signal or conversation including images of at least one of the one or more hand gestures extracting one or more spectral features from the one or more unprocessed images using a Gabor filter bank, receiving the one or more unprocessed images of the one or more hand gestures and the extracted one or more spectral features by a Convolution Neural Network (CNN), and outputting a classification for the at least one of the one or more hand gestures using the Convolution Neural Network (CNN). In some implementations, at least one of the extracted one or more spectral features and at least one of the one or more unprocessed images of the one or more hand gestures are concatenated and input to the Convolution Neural Network (CNN).

Methods and systems for computerized recognition of hand gestures

Methods, systems, and computer readable media for machine translation between Arabic language and Arabic Sign Language are described.

Methods, computer readable media, and systems for machine translation between Arabic and Arabic sign language

A method for Arabic and Farsi font recognition for determining the font of text using a nearest neighbor classifier, where the classifier uses a combination of features including: box counting dimension, center of gravity, the number of vertical and horizontal extrema, the number of black and white components, the smallest black component, the Log baseline position, concave curvature features, convex curvature features, direction and direction length features, Log-Gabor features, and segmented Log-Gabor features. The method is tested using various combination of features on various text fonts, sizes, and styles. It is observed the segmented Log-Gabor features produce a 99.85% font recognition rate, and the combination of all non-Log-Gabor features produces a 97.96% font recognition rate.

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Dhahran, Saudi Arabia

Hamzah Luqman