Abstract: Disclosed are methods, apparatus and systems for real-time gesture recognition. One exemplary method for the real-time identification of a gesture communicated by a subject includes receiving, by a first thread of the one or more multi-threaded processors, a first set of image frames associated with the gesture, the first set of image frames captured during a first time interval, performing, by the first thread, pose estimation on each frame of the first set of image frames including eliminating background information from each frame to obtain one or more areas of interest, storing information representative of the one or more areas of interest in a shared memory accessible to the one or more multi-threaded processors, and performing, by a second thread of the one or more multi-threaded processors, a gesture recognition operation on a second set of image frames associated with the gesture.

Abstract: Methods, apparatus and systems for recognizing sign language movements using multiple input and output modalities. One example method includes capturing a movement associated with the sign language using a set of visual sensing devices, the set of visual sensing devices comprising multiple apertures oriented with respect to the subject to receive optical signals corresponding to the movement from multiple angles, generating digital information corresponding to the movement based on the optical signals from the multiple angles, collecting depth information corresponding to the movement in one or more planes perpendicular to an image plane captured by the set of visual sensing devices, producing a reduced set of digital information by removing at least some of the digital information based on the depth information, generating a composite digital representation by aligning at least a portion of the reduced set of digital information, and recognizing the movement based on the composite digital representation.

Abstract: Disclosed are methods, apparatus and systems for improving data management and workload distribution in pattern recognition systems. An example method of managing data for a sign language translation system includes receiving multiple sets of data acquired by one or more data acquisition devices. Each set of data including an image frame that illustrates at least a part of a gesture. The method includes determining, for each of the multiple sets of data, a plurality of attribute values defined by a customized template. The method includes accessing the multiple sets of data, by a plurality of processing units, based on a location indicated by the attributes for recognizing the at least a part of a gesture.

Abstract: Disclosed are methods, apparatus and systems for gesture recognition based on neural network processing. One exemplary method for identifying a gesture communicated by a subject includes receiving a plurality of images associated with the gesture, providing the plurality of images to a first 3-dimensional convolutional neural network (3D CNN) and a second 3D CNN, where the first 3D CNN is operable to produce motion information, where the second 3D CNN is operable to produce pose and color information, and where the first 3D CNN is operable to implement an optical flow algorithm to detect the gesture, fusing the motion information and the pose and color information to produce an identification of the gesture, and determining whether the identification corresponds to a singular gesture across the plurality of images using a recurrent neural network that comprises one or more long short-term memory units.

Abstract: Methods, devices and systems for training a pattern recognition system are described. In one example, a method for training a sign language translation system includes generating a three-dimensional (3D) scene that includes a 3D model simulating a gesture that represents a letter, a word, or a phrase in a sign language. The method includes obtaining a value indicative of a total number of training images to be generated, using the value indicative of the total number of training images to determine a plurality of variations of the 3D scene for generating of the training images, applying each of plurality of variations to the 3D scene to produce a plurality of modified 3D scenes, and capturing an image of each of the plurality of modified 3D scenes to form the training images for a neural network of the sign language translation system.